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Creation (random)
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<code class="doc-symbol doc-symbol-toc doc-symbol-method"></code>&nbsp;manual_seed
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<article class="md-content__inner md-typeset">
<a href="https://github.com/tinygrad/tinygrad/edit/master/docs/tensor/creation.md" title="Edit this page" class="md-content__button md-icon" rel="edit">
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<h1>Creation</h1>
<h2 id="creation-basic">Creation (basic)<a class="headerlink" href="#creation-basic" title="Permanent link">¤</a></h2>
<div class="doc doc-object doc-function">
<h3 id="tinygrad.Tensor.empty" class="doc doc-heading">
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">empty</span>
<span class="doc doc-labels">
<small class="doc doc-label doc-label-staticmethod"><code>staticmethod</code></small>
</span>
<a href="#tinygrad.Tensor.empty" class="headerlink" title="Permanent link">¤</a></h3>
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">empty</span><span class="p">(</span>
<span class="o">*</span><span class="n">shape</span><span class="p">,</span>
<span class="n">device</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/stdtypes.html#str">str</a></span> <span class="o">|</span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/stdtypes.html#tuple">tuple</a></span><span class="p">[</span><span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/stdtypes.html#str">str</a></span><span class="p">,</span> <span class="o">...</span><span class="p">]</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">dtype</span><span class="p">:</span> <span class="n"><span title="tinygrad.dtype.DTypeLike">DTypeLike</span></span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="o">**</span><span class="n">kwargs</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n"><a class="autorefs autorefs-internal" title="&lt;code class=&quot;doc-symbol doc-symbol-heading doc-symbol-class&quot;&gt;&lt;/code&gt; &lt;span class=&quot;doc doc-object-name doc-class-name&quot;&gt;Tensor&lt;/span&gt; (&lt;code&gt;tinygrad.tensor.Tensor&lt;/code&gt;)" href="../#tinygrad.Tensor">Tensor</a></span>
</code></pre></div>
<div class="doc doc-contents first">
<p>Creates an empty tensor with the given shape.</p>
<p>You can pass in <code class="language-python highlight"><span class="n">dtype</span></code> and <code class="language-python highlight"><span class="n">device</span></code> keyword arguments to control the data type and device of the tensor.
Additionally, all other keyword arguments are passed to the constructor of the tensor.</p>
<div class="language-python highlight"><pre><span></span><code><span class="n">t</span> <span class="o">=</span> <span class="n">Tensor</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">t</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
</code></pre></div>
<div class="language-python highlight"><pre><span></span><code><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
</code></pre></div>
<details class="mkdocstrings-source">
<summary>Source code in <code>tinygrad/tensor.py</code></summary>
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">482</span>
<span class="normal">483</span>
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<span class="normal">499</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="nd">@staticmethod</span>
<span class="k">def</span><span class="w"> </span><span class="nf">empty</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="n">device</span><span class="p">:</span><span class="nb">str</span><span class="o">|</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="o">...</span><span class="p">]</span><span class="o">|</span><span class="kc">None</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dtype</span><span class="p">:</span><span class="n">DTypeLike</span><span class="o">|</span><span class="kc">None</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tensor</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Creates an empty tensor with the given shape.</span>
<span class="sd"> You can pass in `dtype` and `device` keyword arguments to control the data type and device of the tensor.</span>
<span class="sd"> Additionally, all other keyword arguments are passed to the constructor of the tensor.</span>
<span class="sd"> ```python exec=&quot;true&quot; source=&quot;above&quot; session=&quot;tensor&quot; result=&quot;python&quot;</span>
<span class="sd"> t = Tensor.empty(2, 3)</span>
<span class="sd"> print(t.shape)</span>
<span class="sd"> ```</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">dtype</span><span class="p">,</span> <span class="n">shape</span> <span class="o">=</span> <span class="n">to_dtype</span><span class="p">(</span><span class="n">dtype</span><span class="p">)</span> <span class="k">if</span> <span class="n">dtype</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">dtypes</span><span class="o">.</span><span class="n">default_float</span><span class="p">,</span> <span class="n">argfix</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">)</span>
<span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">size</span><span class="o">:=</span><span class="n">prod</span><span class="p">([</span><span class="n">x</span><span class="o">.</span><span class="n">vmax</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">UOp</span><span class="p">)</span> <span class="k">else</span> <span class="n">x</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">shape</span><span class="p">]),</span> <span class="nb">int</span><span class="p">):</span> <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;size must be int </span><span class="si">{</span><span class="n">size</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
<span class="c1"># TODO: add test for multidevice tensor</span>
<span class="n">device</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">canonicalize_device</span><span class="p">(</span><span class="n">d</span><span class="p">)</span> <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="n">device</span><span class="p">)</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">device</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">)</span> <span class="k">else</span> <span class="n">canonicalize_device</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
<span class="k">return</span> <span class="n">Tensor</span><span class="p">(</span><span class="n">UOp</span><span class="o">.</span><span class="n">new_buffer</span><span class="p">(</span><span class="n">device</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="n">dtype</span><span class="p">),</span> <span class="n">device</span><span class="p">,</span> <span class="n">dtype</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span><span class="o">.</span><span class="n">shrink</span><span class="p">(((</span><span class="mi">0</span><span class="p">,</span><span class="n">prod</span><span class="p">(</span><span class="n">shape</span><span class="p">)),))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span>
</code></pre></div></td></tr></table></div>
</details>
</div>
</div>
<div class="doc doc-object doc-function">
<h3 id="tinygrad.Tensor.zeros" class="doc doc-heading">
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">zeros</span>
<span class="doc doc-labels">
<small class="doc doc-label doc-label-staticmethod"><code>staticmethod</code></small>
</span>
<a href="#tinygrad.Tensor.zeros" class="headerlink" title="Permanent link">¤</a></h3>
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">zeros</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n"><a class="autorefs autorefs-internal" title="&lt;code class=&quot;doc-symbol doc-symbol-heading doc-symbol-class&quot;&gt;&lt;/code&gt; &lt;span class=&quot;doc doc-object-name doc-class-name&quot;&gt;Tensor&lt;/span&gt; (&lt;code&gt;tinygrad.tensor.Tensor&lt;/code&gt;)" href="../#tinygrad.Tensor">Tensor</a></span>
</code></pre></div>
<div class="doc doc-contents first">
<p>Creates a tensor with the given shape, filled with zeros.</p>
<p>You can pass in <code class="language-python highlight"><span class="n">dtype</span></code> and <code class="language-python highlight"><span class="n">device</span></code> keyword arguments to control the data type and device of the tensor.
Additionally, all other keyword arguments are passed to the constructor of the tensor.</p>
<p><div class="language-python highlight"><pre><span></span><code><span class="nb">print</span><span class="p">(</span><span class="n">Tensor</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
</code></pre></div>
<div class="language-python highlight"><pre><span></span><code><span class="p">[[</span><span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]</span>
<span class="p">[</span><span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]]</span>
</code></pre></div>
<div class="language-python highlight"><pre><span></span><code><span class="nb">print</span><span class="p">(</span><span class="n">Tensor</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtypes</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
</code></pre></div>
<div class="language-python highlight"><pre><span></span><code><span class="p">[[</span><span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span><span class="p">]</span>
<span class="p">[</span><span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span><span class="p">]]</span>
</code></pre></div></p>
<details class="mkdocstrings-source">
<summary>Source code in <code>tinygrad/tensor.py</code></summary>
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">631</span>
<span class="normal">632</span>
<span class="normal">633</span>
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<span class="normal">635</span>
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<span class="normal">637</span>
<span class="normal">638</span>
<span class="normal">639</span>
<span class="normal">640</span>
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<span class="normal">643</span>
<span class="normal">644</span>
<span class="normal">645</span>
<span class="normal">646</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="nd">@staticmethod</span>
<span class="k">def</span><span class="w"> </span><span class="nf">zeros</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tensor</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Creates a tensor with the given shape, filled with zeros.</span>
<span class="sd"> You can pass in `dtype` and `device` keyword arguments to control the data type and device of the tensor.</span>
<span class="sd"> Additionally, all other keyword arguments are passed to the constructor of the tensor.</span>
<span class="sd"> ```python exec=&quot;true&quot; source=&quot;above&quot; session=&quot;tensor&quot; result=&quot;python&quot;</span>
<span class="sd"> print(Tensor.zeros(2, 3).numpy())</span>
<span class="sd"> ```</span>
<span class="sd"> ```python exec=&quot;true&quot; source=&quot;above&quot; session=&quot;tensor&quot; result=&quot;python&quot;</span>
<span class="sd"> print(Tensor.zeros(2, 3, dtype=dtypes.int32).numpy())</span>
<span class="sd"> ```</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="n">Tensor</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">argfix</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">),</span> <span class="mf">0.0</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
</code></pre></div></td></tr></table></div>
</details>
</div>
</div>
<div class="doc doc-object doc-function">
<h3 id="tinygrad.Tensor.ones" class="doc doc-heading">
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">ones</span>
<span class="doc doc-labels">
<small class="doc doc-label doc-label-staticmethod"><code>staticmethod</code></small>
</span>
<a href="#tinygrad.Tensor.ones" class="headerlink" title="Permanent link">¤</a></h3>
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">ones</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n"><a class="autorefs autorefs-internal" title="&lt;code class=&quot;doc-symbol doc-symbol-heading doc-symbol-class&quot;&gt;&lt;/code&gt; &lt;span class=&quot;doc doc-object-name doc-class-name&quot;&gt;Tensor&lt;/span&gt; (&lt;code&gt;tinygrad.tensor.Tensor&lt;/code&gt;)" href="../#tinygrad.Tensor">Tensor</a></span>
</code></pre></div>
<div class="doc doc-contents first">
<p>Creates a tensor with the given shape, filled with ones.</p>
<p>You can pass in <code class="language-python highlight"><span class="n">dtype</span></code> and <code class="language-python highlight"><span class="n">device</span></code> keyword arguments to control the data type and device of the tensor.
Additionally, all other keyword arguments are passed to the constructor of the tensor.</p>
<p><div class="language-python highlight"><pre><span></span><code><span class="nb">print</span><span class="p">(</span><span class="n">Tensor</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
</code></pre></div>
<div class="language-python highlight"><pre><span></span><code><span class="p">[[</span><span class="mf">1.</span> <span class="mf">1.</span> <span class="mf">1.</span><span class="p">]</span>
<span class="p">[</span><span class="mf">1.</span> <span class="mf">1.</span> <span class="mf">1.</span><span class="p">]]</span>
</code></pre></div>
<div class="language-python highlight"><pre><span></span><code><span class="nb">print</span><span class="p">(</span><span class="n">Tensor</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtypes</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
</code></pre></div>
<div class="language-python highlight"><pre><span></span><code><span class="p">[[</span><span class="mi">1</span> <span class="mi">1</span> <span class="mi">1</span><span class="p">]</span>
<span class="p">[</span><span class="mi">1</span> <span class="mi">1</span> <span class="mi">1</span><span class="p">]]</span>
</code></pre></div></p>
<details class="mkdocstrings-source">
<summary>Source code in <code>tinygrad/tensor.py</code></summary>
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">648</span>
<span class="normal">649</span>
<span class="normal">650</span>
<span class="normal">651</span>
<span class="normal">652</span>
<span class="normal">653</span>
<span class="normal">654</span>
<span class="normal">655</span>
<span class="normal">656</span>
<span class="normal">657</span>
<span class="normal">658</span>
<span class="normal">659</span>
<span class="normal">660</span>
<span class="normal">661</span>
<span class="normal">662</span>
<span class="normal">663</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="nd">@staticmethod</span>
<span class="k">def</span><span class="w"> </span><span class="nf">ones</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tensor</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Creates a tensor with the given shape, filled with ones.</span>
<span class="sd"> You can pass in `dtype` and `device` keyword arguments to control the data type and device of the tensor.</span>
<span class="sd"> Additionally, all other keyword arguments are passed to the constructor of the tensor.</span>
<span class="sd"> ```python exec=&quot;true&quot; source=&quot;above&quot; session=&quot;tensor&quot; result=&quot;python&quot;</span>
<span class="sd"> print(Tensor.ones(2, 3).numpy())</span>
<span class="sd"> ```</span>
<span class="sd"> ```python exec=&quot;true&quot; source=&quot;above&quot; session=&quot;tensor&quot; result=&quot;python&quot;</span>
<span class="sd"> print(Tensor.ones(2, 3, dtype=dtypes.int32).numpy())</span>
<span class="sd"> ```</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="n">Tensor</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">argfix</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">),</span> <span class="mf">1.0</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
</code></pre></div></td></tr></table></div>
</details>
</div>
</div>
<div class="doc doc-object doc-function">
<h3 id="tinygrad.Tensor.full" class="doc doc-heading">
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">full</span>
<span class="doc doc-labels">
<small class="doc doc-label doc-label-staticmethod"><code>staticmethod</code></small>
</span>
<a href="#tinygrad.Tensor.full" class="headerlink" title="Permanent link">¤</a></h3>
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">full</span><span class="p">(</span>
<span class="n">shape</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/stdtypes.html#tuple">tuple</a></span><span class="p">[</span><span class="n"><span title="tinygrad.uop.ops.sint">sint</span></span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="n">fill_value</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-internal" title="&lt;code class=&quot;doc-symbol doc-symbol-heading doc-symbol-attribute&quot;&gt;&lt;/code&gt; &lt;span class=&quot;doc doc-object-name doc-attribute-name&quot;&gt;ConstType&lt;/span&gt;
&lt;span class=&quot;doc doc-labels&quot;&gt;
&lt;small class=&quot;doc doc-label doc-label-module-attribute&quot;&gt;&lt;code&gt;module-attribute&lt;/code&gt;&lt;/small&gt;
&lt;/span&gt; (&lt;code&gt;tinygrad.dtype.ConstType&lt;/code&gt;)" href="../../dtypes/#tinygrad.dtype.ConstType">ConstType</a></span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n"><a class="autorefs autorefs-internal" title="&lt;code class=&quot;doc-symbol doc-symbol-heading doc-symbol-class&quot;&gt;&lt;/code&gt; &lt;span class=&quot;doc doc-object-name doc-class-name&quot;&gt;Tensor&lt;/span&gt; (&lt;code&gt;tinygrad.tensor.Tensor&lt;/code&gt;)" href="../#tinygrad.Tensor">Tensor</a></span>
</code></pre></div>
<div class="doc doc-contents first">
<p>Creates a tensor with the given shape, filled with the given value.</p>
<p>You can pass in <code class="language-python highlight"><span class="n">dtype</span></code> and <code class="language-python highlight"><span class="n">device</span></code> keyword arguments to control the data type and device of the tensor.
Additionally, all other keyword arguments are passed to the constructor of the tensor.</p>
<p><div class="language-python highlight"><pre><span></span><code><span class="nb">print</span><span class="p">(</span><span class="n">Tensor</span><span class="o">.</span><span class="n">full</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="mi">42</span><span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
</code></pre></div>
<div class="language-python highlight"><pre><span></span><code><span class="p">[[</span><span class="mi">42</span> <span class="mi">42</span> <span class="mi">42</span><span class="p">]</span>
<span class="p">[</span><span class="mi">42</span> <span class="mi">42</span> <span class="mi">42</span><span class="p">]]</span>
</code></pre></div>
<div class="language-python highlight"><pre><span></span><code><span class="nb">print</span><span class="p">(</span><span class="n">Tensor</span><span class="o">.</span><span class="n">full</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
</code></pre></div>
<div class="language-python highlight"><pre><span></span><code><span class="p">[[</span><span class="kc">False</span> <span class="kc">False</span> <span class="kc">False</span><span class="p">]</span>
<span class="p">[</span><span class="kc">False</span> <span class="kc">False</span> <span class="kc">False</span><span class="p">]]</span>
</code></pre></div></p>
<details class="mkdocstrings-source">
<summary>Source code in <code>tinygrad/tensor.py</code></summary>
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">614</span>
<span class="normal">615</span>
<span class="normal">616</span>
<span class="normal">617</span>
<span class="normal">618</span>
<span class="normal">619</span>
<span class="normal">620</span>
<span class="normal">621</span>
<span class="normal">622</span>
<span class="normal">623</span>
<span class="normal">624</span>
<span class="normal">625</span>
<span class="normal">626</span>
<span class="normal">627</span>
<span class="normal">628</span>
<span class="normal">629</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="nd">@staticmethod</span>
<span class="k">def</span><span class="w"> </span><span class="nf">full</span><span class="p">(</span><span class="n">shape</span><span class="p">:</span><span class="nb">tuple</span><span class="p">[</span><span class="n">sint</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="n">fill_value</span><span class="p">:</span><span class="n">ConstType</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tensor</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Creates a tensor with the given shape, filled with the given value.</span>
<span class="sd"> You can pass in `dtype` and `device` keyword arguments to control the data type and device of the tensor.</span>
<span class="sd"> Additionally, all other keyword arguments are passed to the constructor of the tensor.</span>
<span class="sd"> ```python exec=&quot;true&quot; source=&quot;above&quot; session=&quot;tensor&quot; result=&quot;python&quot;</span>
<span class="sd"> print(Tensor.full((2, 3), 42).numpy())</span>
<span class="sd"> ```</span>
<span class="sd"> ```python exec=&quot;true&quot; source=&quot;above&quot; session=&quot;tensor&quot; result=&quot;python&quot;</span>
<span class="sd"> print(Tensor.full((2, 3), False).numpy())</span>
<span class="sd"> ```</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="n">Tensor</span><span class="p">(</span><span class="n">fill_value</span><span class="p">,</span> <span class="n">_force_unique</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="p">)</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="n">new_shape</span> <span class="o">:=</span> <span class="n">argfix</span><span class="p">(</span><span class="n">shape</span><span class="p">)))</span><span class="o">.</span><span class="n">expand</span><span class="p">(</span><span class="n">new_shape</span><span class="p">)</span>
</code></pre></div></td></tr></table></div>
</details>
</div>
</div>
<div class="doc doc-object doc-function">
<h3 id="tinygrad.Tensor.arange" class="doc doc-heading">
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">arange</span>
<span class="doc doc-labels">
<small class="doc doc-label doc-label-staticmethod"><code>staticmethod</code></small>
</span>
<a href="#tinygrad.Tensor.arange" class="headerlink" title="Permanent link">¤</a></h3>
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">arange</span><span class="p">(</span><span class="n">start</span><span class="p">,</span> <span class="n">stop</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n"><a class="autorefs autorefs-internal" title="&lt;code class=&quot;doc-symbol doc-symbol-heading doc-symbol-class&quot;&gt;&lt;/code&gt; &lt;span class=&quot;doc doc-object-name doc-class-name&quot;&gt;Tensor&lt;/span&gt; (&lt;code&gt;tinygrad.tensor.Tensor&lt;/code&gt;)" href="../#tinygrad.Tensor">Tensor</a></span>
</code></pre></div>
<div class="doc doc-contents first">
<p>Returns a 1-D tensor of size <code class="language-python highlight"><span class="n">ceil</span><span class="p">((</span><span class="n">stop</span> <span class="o">-</span> <span class="n">start</span><span class="p">)</span> <span class="o">/</span> <span class="n">step</span><span class="p">)</span></code> with values from <code class="language-python highlight"><span class="p">[</span><span class="n">start</span><span class="p">,</span> <span class="n">stop</span><span class="p">)</span></code>, with spacing between values given by <code class="language-python highlight"><span class="n">step</span></code>.</p>
<p>If <code class="language-python highlight"><span class="n">stop</span></code> is not specified, values are generated from <code class="language-python highlight"><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="n">start</span><span class="p">)</span></code> with the given <code class="language-python highlight"><span class="n">step</span></code>.</p>
<p>If <code class="language-python highlight"><span class="n">stop</span></code> is specified, values are generated from <code class="language-python highlight"><span class="p">[</span><span class="n">start</span><span class="p">,</span> <span class="n">stop</span><span class="p">)</span></code> with the given <code class="language-python highlight"><span class="n">step</span></code>.</p>
<p>You can pass in <code class="language-python highlight"><span class="n">dtype</span></code> and <code class="language-python highlight"><span class="n">device</span></code> keyword arguments to control the data type and device of the tensor.
Additionally, all other keyword arguments are passed to the constructor of the tensor.</p>
<p><div class="language-python highlight"><pre><span></span><code><span class="nb">print</span><span class="p">(</span><span class="n">Tensor</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
</code></pre></div>
<div class="language-python highlight"><pre><span></span><code><span class="p">[</span><span class="mi">0</span> <span class="mi">1</span> <span class="mi">2</span> <span class="mi">3</span> <span class="mi">4</span><span class="p">]</span>
</code></pre></div>
<div class="language-python highlight"><pre><span></span><code><span class="nb">print</span><span class="p">(</span><span class="n">Tensor</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
</code></pre></div>
<div class="language-python highlight"><pre><span></span><code><span class="p">[</span><span class="mi">5</span> <span class="mi">6</span> <span class="mi">7</span> <span class="mi">8</span> <span class="mi">9</span><span class="p">]</span>
</code></pre></div>
<div class="language-python highlight"><pre><span></span><code><span class="nb">print</span><span class="p">(</span><span class="n">Tensor</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
</code></pre></div>
<div class="language-python highlight"><pre><span></span><code><span class="p">[</span><span class="mi">5</span> <span class="mi">7</span> <span class="mi">9</span><span class="p">]</span>
</code></pre></div>
<div class="language-python highlight"><pre><span></span><code><span class="nb">print</span><span class="p">(</span><span class="n">Tensor</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">5.5</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
</code></pre></div>
<div class="language-python highlight"><pre><span></span><code><span class="p">[</span><span class="mf">5.5</span> <span class="mf">7.5</span> <span class="mf">9.5</span><span class="p">]</span>
</code></pre></div></p>
<details class="mkdocstrings-source">
<summary>Source code in <code>tinygrad/tensor.py</code></summary>
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">665</span>
<span class="normal">666</span>
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<span class="normal">674</span>
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<span class="normal">685</span>
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<span class="normal">687</span>
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<span class="normal">694</span>
<span class="normal">695</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="nd">@staticmethod</span>
<span class="k">def</span><span class="w"> </span><span class="nf">arange</span><span class="p">(</span><span class="n">start</span><span class="p">,</span> <span class="n">stop</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tensor</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns a 1-D tensor of size `ceil((stop - start) / step)` with values from `[start, stop)`, with spacing between values given by `step`.</span>
<span class="sd"> If `stop` is not specified, values are generated from `[0, start)` with the given `step`.</span>
<span class="sd"> If `stop` is specified, values are generated from `[start, stop)` with the given `step`.</span>
<span class="sd"> You can pass in `dtype` and `device` keyword arguments to control the data type and device of the tensor.</span>
<span class="sd"> Additionally, all other keyword arguments are passed to the constructor of the tensor.</span>
<span class="sd"> ```python exec=&quot;true&quot; source=&quot;above&quot; session=&quot;tensor&quot; result=&quot;python&quot;</span>
<span class="sd"> print(Tensor.arange(5).numpy())</span>
<span class="sd"> ```</span>
<span class="sd"> ```python exec=&quot;true&quot; source=&quot;above&quot; session=&quot;tensor&quot; result=&quot;python&quot;</span>
<span class="sd"> print(Tensor.arange(5, 10).numpy())</span>
<span class="sd"> ```</span>
<span class="sd"> ```python exec=&quot;true&quot; source=&quot;above&quot; session=&quot;tensor&quot; result=&quot;python&quot;</span>
<span class="sd"> print(Tensor.arange(5, 10, 2).numpy())</span>
<span class="sd"> ```</span>
<span class="sd"> ```python exec=&quot;true&quot; source=&quot;above&quot; session=&quot;tensor&quot; result=&quot;python&quot;</span>
<span class="sd"> print(Tensor.arange(5.5, 10, 2).numpy())</span>
<span class="sd"> ```</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="n">stop</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> <span class="n">stop</span><span class="p">,</span> <span class="n">start</span> <span class="o">=</span> <span class="n">start</span><span class="p">,</span> <span class="mi">0</span>
<span class="n">dtype</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">&quot;dtype&quot;</span><span class="p">,</span> <span class="n">dtypes</span><span class="o">.</span><span class="n">default_float</span> <span class="k">if</span> <span class="nb">any</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="nb">float</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="p">(</span><span class="n">start</span><span class="p">,</span> <span class="n">stop</span><span class="p">,</span> <span class="n">step</span><span class="p">))</span> <span class="k">else</span> <span class="n">dtypes</span><span class="o">.</span><span class="n">default_int</span><span class="p">)</span>
<span class="k">if</span> <span class="n">start</span> <span class="o">&lt;</span> <span class="p">(</span><span class="n">dt</span><span class="o">:=</span><span class="n">to_dtype</span><span class="p">(</span><span class="n">dtype</span><span class="p">))</span><span class="o">.</span><span class="n">min</span> <span class="ow">or</span> <span class="n">dt</span><span class="o">.</span><span class="n">max</span> <span class="o">&lt;</span> <span class="p">(</span><span class="n">stop</span><span class="o">-</span><span class="n">step</span><span class="p">):</span> <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;arange [</span><span class="si">{</span><span class="n">start</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="n">stop</span><span class="si">}</span><span class="s2">) is not representable in dtype </span><span class="si">{</span><span class="n">dtype</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
<span class="c1"># NOTE: this matches numpy, torch raises RuntimeError if stop-start and step have different signs</span>
<span class="k">if</span> <span class="p">(</span><span class="n">output_len</span><span class="o">:=</span><span class="n">ceildiv</span><span class="p">(</span><span class="n">stop</span><span class="o">-</span><span class="n">start</span><span class="p">,</span> <span class="n">step</span><span class="p">))</span> <span class="o">&lt;=</span> <span class="mi">0</span><span class="p">:</span> <span class="k">return</span> <span class="n">Tensor</span><span class="p">([],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="k">return</span> <span class="p">(</span><span class="n">Tensor</span><span class="o">.</span><span class="n">full</span><span class="p">((</span><span class="n">output_len</span><span class="p">,),</span> <span class="n">step</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span><span class="o">.</span><span class="n">_cumalu</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">Ops</span><span class="o">.</span><span class="n">ADD</span><span class="p">)</span> <span class="o">+</span> <span class="p">(</span><span class="n">start</span> <span class="o">-</span> <span class="n">step</span><span class="p">))</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="n">dtype</span><span class="p">)</span>
</code></pre></div></td></tr></table></div>
</details>
</div>
</div>
<div class="doc doc-object doc-function">
<h3 id="tinygrad.Tensor.linspace" class="doc doc-heading">
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">linspace</span>
<span class="doc doc-labels">
<small class="doc doc-label doc-label-staticmethod"><code>staticmethod</code></small>
</span>
<a href="#tinygrad.Tensor.linspace" class="headerlink" title="Permanent link">¤</a></h3>
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">linspace</span><span class="p">(</span>
<span class="n">start</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-internal" title="&lt;code class=&quot;doc-symbol doc-symbol-heading doc-symbol-method&quot;&gt;&lt;/code&gt; &lt;span class=&quot;doc doc-object-name doc-function-name&quot;&gt;int&lt;/span&gt; (&lt;code&gt;tinygrad.tensor.Tensor.int&lt;/code&gt;)" href="../elementwise/#tinygrad.Tensor.int">int</a></span> <span class="o">|</span> <span class="n"><a class="autorefs autorefs-internal" title="&lt;code class=&quot;doc-symbol doc-symbol-heading doc-symbol-method&quot;&gt;&lt;/code&gt; &lt;span class=&quot;doc doc-object-name doc-function-name&quot;&gt;float&lt;/span&gt; (&lt;code&gt;tinygrad.tensor.Tensor.float&lt;/code&gt;)" href="../elementwise/#tinygrad.Tensor.float">float</a></span><span class="p">,</span>
<span class="n">stop</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-internal" title="&lt;code class=&quot;doc-symbol doc-symbol-heading doc-symbol-method&quot;&gt;&lt;/code&gt; &lt;span class=&quot;doc doc-object-name doc-function-name&quot;&gt;int&lt;/span&gt; (&lt;code&gt;tinygrad.tensor.Tensor.int&lt;/code&gt;)" href="../elementwise/#tinygrad.Tensor.int">int</a></span> <span class="o">|</span> <span class="n"><a class="autorefs autorefs-internal" title="&lt;code class=&quot;doc-symbol doc-symbol-heading doc-symbol-method&quot;&gt;&lt;/code&gt; &lt;span class=&quot;doc doc-object-name doc-function-name&quot;&gt;float&lt;/span&gt; (&lt;code&gt;tinygrad.tensor.Tensor.float&lt;/code&gt;)" href="../elementwise/#tinygrad.Tensor.float">float</a></span><span class="p">,</span>
<span class="n">steps</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-internal" title="&lt;code class=&quot;doc-symbol doc-symbol-heading doc-symbol-method&quot;&gt;&lt;/code&gt; &lt;span class=&quot;doc doc-object-name doc-function-name&quot;&gt;int&lt;/span&gt; (&lt;code&gt;tinygrad.tensor.Tensor.int&lt;/code&gt;)" href="../elementwise/#tinygrad.Tensor.int">int</a></span><span class="p">,</span>
<span class="o">**</span><span class="n">kwargs</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n"><a class="autorefs autorefs-internal" title="&lt;code class=&quot;doc-symbol doc-symbol-heading doc-symbol-class&quot;&gt;&lt;/code&gt; &lt;span class=&quot;doc doc-object-name doc-class-name&quot;&gt;Tensor&lt;/span&gt; (&lt;code&gt;tinygrad.tensor.Tensor&lt;/code&gt;)" href="../#tinygrad.Tensor">Tensor</a></span>
</code></pre></div>
<div class="doc doc-contents first">
<p>Returns a 1-D tensor of <code class="language-python highlight"><span class="n">steps</span></code> evenly spaced values from <code class="language-python highlight"><span class="n">start</span></code> to <code class="language-python highlight"><span class="n">stop</span></code>, inclusive.</p>
<p>You can pass in <code class="language-python highlight"><span class="n">dtype</span></code> and <code class="language-python highlight"><span class="n">device</span></code> keyword arguments to control the data type and device of the tensor.
Additionally, all other keyword arguments are passed to the constructor of the tensor.</p>
<p><div class="language-python highlight"><pre><span></span><code><span class="nb">print</span><span class="p">(</span><span class="n">Tensor</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
</code></pre></div>
<div class="language-python highlight"><pre><span></span><code><span class="p">[</span> <span class="mf">0.</span> <span class="mf">2.5</span> <span class="mf">5.</span> <span class="mf">7.5</span> <span class="mf">10.</span> <span class="p">]</span>
</code></pre></div>
<div class="language-python highlight"><pre><span></span><code><span class="nb">print</span><span class="p">(</span><span class="n">Tensor</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
</code></pre></div>
<div class="language-python highlight"><pre><span></span><code><span class="p">[</span><span class="o">-</span><span class="mf">1.</span> <span class="o">-</span><span class="mf">0.5</span> <span class="mf">0.</span> <span class="mf">0.5</span> <span class="mf">1.</span> <span class="p">]</span>
</code></pre></div></p>
<details class="mkdocstrings-source">
<summary>Source code in <code>tinygrad/tensor.py</code></summary>
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">697</span>
<span class="normal">698</span>
<span class="normal">699</span>
<span class="normal">700</span>
<span class="normal">701</span>
<span class="normal">702</span>
<span class="normal">703</span>
<span class="normal">704</span>
<span class="normal">705</span>
<span class="normal">706</span>
<span class="normal">707</span>
<span class="normal">708</span>
<span class="normal">709</span>
<span class="normal">710</span>
<span class="normal">711</span>
<span class="normal">712</span>
<span class="normal">713</span>
<span class="normal">714</span>
<span class="normal">715</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="nd">@staticmethod</span>
<span class="k">def</span><span class="w"> </span><span class="nf">linspace</span><span class="p">(</span><span class="n">start</span><span class="p">:</span><span class="nb">int</span><span class="o">|</span><span class="nb">float</span><span class="p">,</span> <span class="n">stop</span><span class="p">:</span><span class="nb">int</span><span class="o">|</span><span class="nb">float</span><span class="p">,</span> <span class="n">steps</span><span class="p">:</span><span class="nb">int</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tensor</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns a 1-D tensor of `steps` evenly spaced values from `start` to `stop`, inclusive.</span>
<span class="sd"> You can pass in `dtype` and `device` keyword arguments to control the data type and device of the tensor.</span>
<span class="sd"> Additionally, all other keyword arguments are passed to the constructor of the tensor.</span>
<span class="sd"> ```python exec=&quot;true&quot; source=&quot;above&quot; session=&quot;tensor&quot; result=&quot;python&quot;</span>
<span class="sd"> print(Tensor.linspace(0, 10, 5).numpy())</span>
<span class="sd"> ```</span>
<span class="sd"> ```python exec=&quot;true&quot; source=&quot;above&quot; session=&quot;tensor&quot; result=&quot;python&quot;</span>
<span class="sd"> print(Tensor.linspace(-1, 1, 5).numpy())</span>
<span class="sd"> ```</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="n">steps</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span> <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;number of steps must be non-negative&quot;</span><span class="p">)</span>
<span class="k">if</span> <span class="p">(</span><span class="n">dtype</span> <span class="o">:=</span> <span class="n">to_dtype</span><span class="p">(</span><span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">&quot;dtype&quot;</span><span class="p">,</span> <span class="n">dtypes</span><span class="o">.</span><span class="n">default_float</span><span class="p">)))</span> <span class="o">==</span> <span class="n">dtypes</span><span class="o">.</span><span class="n">bool</span><span class="p">:</span> <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;linspace with bool dtype is not supported&quot;</span><span class="p">)</span>
<span class="k">if</span> <span class="n">steps</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span> <span class="k">return</span> <span class="n">Tensor</span><span class="p">([</span><span class="n">start</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="k">return</span> <span class="p">(</span><span class="n">start</span> <span class="o">+</span> <span class="n">Tensor</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">*</span> <span class="p">((</span><span class="n">stop</span> <span class="o">-</span> <span class="n">start</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">steps</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)))</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="n">dtype</span><span class="p">)</span>
</code></pre></div></td></tr></table></div>
</details>
</div>
</div>
<div class="doc doc-object doc-function">
<h3 id="tinygrad.Tensor.eye" class="doc doc-heading">
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">eye</span>
<span class="doc doc-labels">
<small class="doc doc-label doc-label-staticmethod"><code>staticmethod</code></small>
</span>
<a href="#tinygrad.Tensor.eye" class="headerlink" title="Permanent link">¤</a></h3>
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">eye</span><span class="p">(</span>
<span class="n">n</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-internal" title="&lt;code class=&quot;doc-symbol doc-symbol-heading doc-symbol-method&quot;&gt;&lt;/code&gt; &lt;span class=&quot;doc doc-object-name doc-function-name&quot;&gt;int&lt;/span&gt; (&lt;code&gt;tinygrad.tensor.Tensor.int&lt;/code&gt;)" href="../elementwise/#tinygrad.Tensor.int">int</a></span><span class="p">,</span>
<span class="n">m</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-internal" title="&lt;code class=&quot;doc-symbol doc-symbol-heading doc-symbol-method&quot;&gt;&lt;/code&gt; &lt;span class=&quot;doc doc-object-name doc-function-name&quot;&gt;int&lt;/span&gt; (&lt;code&gt;tinygrad.tensor.Tensor.int&lt;/code&gt;)" href="../elementwise/#tinygrad.Tensor.int">int</a></span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">dtype</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">device</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">requires_grad</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-internal" title="&lt;code class=&quot;doc-symbol doc-symbol-heading doc-symbol-method&quot;&gt;&lt;/code&gt; &lt;span class=&quot;doc doc-object-name doc-function-name&quot;&gt;bool&lt;/span&gt; (&lt;code&gt;tinygrad.tensor.Tensor.bool&lt;/code&gt;)" href="../elementwise/#tinygrad.Tensor.bool">bool</a></span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n"><a class="autorefs autorefs-internal" title="&lt;code class=&quot;doc-symbol doc-symbol-heading doc-symbol-class&quot;&gt;&lt;/code&gt; &lt;span class=&quot;doc doc-object-name doc-class-name&quot;&gt;Tensor&lt;/span&gt; (&lt;code&gt;tinygrad.tensor.Tensor&lt;/code&gt;)" href="../#tinygrad.Tensor">Tensor</a></span>
</code></pre></div>
<div class="doc doc-contents first">
<p>Returns a 2-D tensor with <code class="language-python highlight"><span class="n">n</span></code> rows and <code class="language-python highlight"><span class="n">m</span></code> columns, with ones on the diagonal and zeros elsewhere.</p>
<p>You can pass in <code class="language-python highlight"><span class="n">dtype</span></code> and <code class="language-python highlight"><span class="n">device</span></code> keyword arguments to control the data type and device of the tensor.
Additionally, all other keyword arguments are passed to the constructor of the tensor.</p>
<div class="language-python highlight"><pre><span></span><code><span class="nb">print</span><span class="p">(</span><span class="n">Tensor</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
</code></pre></div>
<div class="language-python highlight"><pre><span></span><code><span class="p">[[</span><span class="mf">1.</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]</span>
<span class="p">[</span><span class="mf">0.</span> <span class="mf">1.</span> <span class="mf">0.</span><span class="p">]</span>
<span class="p">[</span><span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">1.</span><span class="p">]]</span>
</code></pre></div>
<div class="language-python highlight"><pre><span></span><code><span class="nb">print</span><span class="p">(</span><span class="n">Tensor</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
</code></pre></div>
<div class="language-python highlight"><pre><span></span><code><span class="p">[[</span><span class="mf">1.</span> <span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]</span>
<span class="p">[</span><span class="mf">0.</span> <span class="mf">1.</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]]</span>
</code></pre></div>
<details class="mkdocstrings-source">
<summary>Source code in <code>tinygrad/tensor.py</code></summary>
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">717</span>
<span class="normal">718</span>
<span class="normal">719</span>
<span class="normal">720</span>
<span class="normal">721</span>
<span class="normal">722</span>
<span class="normal">723</span>
<span class="normal">724</span>
<span class="normal">725</span>
<span class="normal">726</span>
<span class="normal">727</span>
<span class="normal">728</span>
<span class="normal">729</span>
<span class="normal">730</span>
<span class="normal">731</span>
<span class="normal">732</span>
<span class="normal">733</span>
<span class="normal">734</span>
<span class="normal">735</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="nd">@staticmethod</span>
<span class="k">def</span><span class="w"> </span><span class="nf">eye</span><span class="p">(</span><span class="n">n</span><span class="p">:</span><span class="nb">int</span><span class="p">,</span> <span class="n">m</span><span class="p">:</span><span class="nb">int</span><span class="o">|</span><span class="kc">None</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">requires_grad</span><span class="p">:</span><span class="nb">bool</span><span class="o">|</span><span class="kc">None</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tensor</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns a 2-D tensor with `n` rows and `m` columns, with ones on the diagonal and zeros elsewhere.</span>
<span class="sd"> You can pass in `dtype` and `device` keyword arguments to control the data type and device of the tensor.</span>
<span class="sd"> Additionally, all other keyword arguments are passed to the constructor of the tensor.</span>
<span class="sd"> ```python exec=&quot;true&quot; source=&quot;above&quot; session=&quot;tensor&quot; result=&quot;python&quot;</span>
<span class="sd"> print(Tensor.eye(3).numpy())</span>
<span class="sd"> ```</span>
<span class="sd"> ```python exec=&quot;true&quot; source=&quot;above&quot; session=&quot;tensor&quot; result=&quot;python&quot;</span>
<span class="sd"> print(Tensor.eye(2, 4).numpy())</span>
<span class="sd"> ```</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="n">n</span> <span class="o">&lt;</span> <span class="mi">0</span> <span class="ow">or</span> <span class="p">((</span><span class="n">m</span> <span class="o">:=</span> <span class="n">n</span> <span class="k">if</span> <span class="n">m</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">m</span><span class="p">)</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">):</span> <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;cannot have negative </span><span class="si">{</span><span class="n">n</span><span class="si">=}</span><span class="s2">, </span><span class="si">{</span><span class="n">m</span><span class="si">=}</span><span class="s2">&quot;</span><span class="p">)</span>
<span class="n">t</span> <span class="o">=</span> <span class="p">(</span><span class="n">Tensor</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">==</span> <span class="n">Tensor</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">))</span>
<span class="k">return</span> <span class="n">t</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="n">dtype</span> <span class="ow">or</span> <span class="n">dtypes</span><span class="o">.</span><span class="n">default_float</span><span class="p">)</span><span class="o">.</span><span class="n">requires_grad_</span><span class="p">(</span><span class="n">requires_grad</span><span class="p">)</span>
</code></pre></div></td></tr></table></div>
</details>
</div>
</div>
<div class="doc doc-object doc-function">
<h3 id="tinygrad.Tensor.full_like" class="doc doc-heading">
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">full_like</span>
<a href="#tinygrad.Tensor.full_like" class="headerlink" title="Permanent link">¤</a></h3>
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">full_like</span><span class="p">(</span><span class="n">fill_value</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-internal" title="&lt;code class=&quot;doc-symbol doc-symbol-heading doc-symbol-attribute&quot;&gt;&lt;/code&gt; &lt;span class=&quot;doc doc-object-name doc-attribute-name&quot;&gt;ConstType&lt;/span&gt;
&lt;span class=&quot;doc doc-labels&quot;&gt;
&lt;small class=&quot;doc doc-label doc-label-module-attribute&quot;&gt;&lt;code&gt;module-attribute&lt;/code&gt;&lt;/small&gt;
&lt;/span&gt; (&lt;code&gt;tinygrad.dtype.ConstType&lt;/code&gt;)" href="../../dtypes/#tinygrad.dtype.ConstType">ConstType</a></span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n"><a class="autorefs autorefs-internal" title="&lt;code class=&quot;doc-symbol doc-symbol-heading doc-symbol-class&quot;&gt;&lt;/code&gt; &lt;span class=&quot;doc doc-object-name doc-class-name&quot;&gt;Tensor&lt;/span&gt; (&lt;code&gt;tinygrad.tensor.Tensor&lt;/code&gt;)" href="../#tinygrad.Tensor">Tensor</a></span>
</code></pre></div>
<div class="doc doc-contents first">
<p>Creates a tensor with the same shape as <code class="language-python highlight"><span class="bp">self</span></code>, filled with the given value.
If <code class="language-python highlight"><span class="n">dtype</span></code> is not specified, the dtype of <code class="language-python highlight"><span class="bp">self</span></code> is used.</p>
<p>You can pass in the <code class="language-python highlight"><span class="n">device</span></code> keyword argument to control device of the tensor.
Additionally, all other keyword arguments are passed to the constructor of the tensor.</p>
<div class="language-python highlight"><pre><span></span><code><span class="n">t</span> <span class="o">=</span> <span class="n">Tensor</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">Tensor</span><span class="o">.</span><span class="n">full_like</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="mi">42</span><span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
</code></pre></div>
<div class="language-python highlight"><pre><span></span><code><span class="p">[[</span><span class="mf">42.</span> <span class="mf">42.</span> <span class="mf">42.</span><span class="p">]</span>
<span class="p">[</span><span class="mf">42.</span> <span class="mf">42.</span> <span class="mf">42.</span><span class="p">]]</span>
</code></pre></div>
<details class="mkdocstrings-source">
<summary>Source code in <code>tinygrad/tensor.py</code></summary>
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">745</span>
<span class="normal">746</span>
<span class="normal">747</span>
<span class="normal">748</span>
<span class="normal">749</span>
<span class="normal">750</span>
<span class="normal">751</span>
<span class="normal">752</span>
<span class="normal">753</span>
<span class="normal">754</span>
<span class="normal">755</span>
<span class="normal">756</span>
<span class="normal">757</span>
<span class="normal">758</span>
<span class="normal">759</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span><span class="w"> </span><span class="nf">full_like</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">fill_value</span><span class="p">:</span><span class="n">ConstType</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tensor</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Creates a tensor with the same shape as `self`, filled with the given value.</span>
<span class="sd"> If `dtype` is not specified, the dtype of `self` is used.</span>
<span class="sd"> You can pass in the `device` keyword argument to control device of the tensor.</span>
<span class="sd"> Additionally, all other keyword arguments are passed to the constructor of the tensor.</span>
<span class="sd"> ```python exec=&quot;true&quot; source=&quot;above&quot; session=&quot;tensor&quot; result=&quot;python&quot;</span>
<span class="sd"> t = Tensor.ones(2, 3)</span>
<span class="sd"> print(Tensor.full_like(t, 42).numpy())</span>
<span class="sd"> ```</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">):</span> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_multi_like</span><span class="p">(</span><span class="n">Tensor</span><span class="o">.</span><span class="n">full</span><span class="p">,</span> <span class="n">fill_value</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="k">return</span> <span class="n">Tensor</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">fill_value</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">&quot;dtype&quot;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">),</span> <span class="n">device</span><span class="o">=</span><span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">&quot;device&quot;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">),</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
</code></pre></div></td></tr></table></div>
</details>
</div>
</div>
<div class="doc doc-object doc-function">
<h3 id="tinygrad.Tensor.zeros_like" class="doc doc-heading">
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">zeros_like</span>
<a href="#tinygrad.Tensor.zeros_like" class="headerlink" title="Permanent link">¤</a></h3>
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">zeros_like</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n"><a class="autorefs autorefs-internal" title="&lt;code class=&quot;doc-symbol doc-symbol-heading doc-symbol-class&quot;&gt;&lt;/code&gt; &lt;span class=&quot;doc doc-object-name doc-class-name&quot;&gt;Tensor&lt;/span&gt; (&lt;code&gt;tinygrad.tensor.Tensor&lt;/code&gt;)" href="../#tinygrad.Tensor">Tensor</a></span>
</code></pre></div>
<div class="doc doc-contents first">
<p>Creates a tensor with the same shape as <code class="language-python highlight"><span class="bp">self</span></code>, filled with zeros.</p>
<p>You can pass in <code class="language-python highlight"><span class="n">dtype</span></code> and <code class="language-python highlight"><span class="n">device</span></code> keyword arguments to control the data type and device of the tensor.
Additionally, all other keyword arguments are passed to the constructor of the tensor.</p>
<div class="language-python highlight"><pre><span></span><code><span class="n">t</span> <span class="o">=</span> <span class="n">Tensor</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">Tensor</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">t</span><span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
</code></pre></div>
<div class="language-python highlight"><pre><span></span><code><span class="p">[[</span><span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]</span>
<span class="p">[</span><span class="mf">0.</span> <span class="mf">0.</span> <span class="mf">0.</span><span class="p">]]</span>
</code></pre></div>
<details class="mkdocstrings-source">
<summary>Source code in <code>tinygrad/tensor.py</code></summary>
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">761</span>
<span class="normal">762</span>
<span class="normal">763</span>
<span class="normal">764</span>
<span class="normal">765</span>
<span class="normal">766</span>
<span class="normal">767</span>
<span class="normal">768</span>
<span class="normal">769</span>
<span class="normal">770</span>
<span class="normal">771</span>
<span class="normal">772</span>
<span class="normal">773</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span><span class="w"> </span><span class="nf">zeros_like</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tensor</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Creates a tensor with the same shape as `self`, filled with zeros.</span>
<span class="sd"> You can pass in `dtype` and `device` keyword arguments to control the data type and device of the tensor.</span>
<span class="sd"> Additionally, all other keyword arguments are passed to the constructor of the tensor.</span>
<span class="sd"> ```python exec=&quot;true&quot; source=&quot;above&quot; session=&quot;tensor&quot; result=&quot;python&quot;</span>
<span class="sd"> t = Tensor.ones(2, 3)</span>
<span class="sd"> print(Tensor.zeros_like(t).numpy())</span>
<span class="sd"> ```</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">full_like</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
</code></pre></div></td></tr></table></div>
</details>
</div>
</div>
<div class="doc doc-object doc-function">
<h3 id="tinygrad.Tensor.ones_like" class="doc doc-heading">
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">ones_like</span>
<a href="#tinygrad.Tensor.ones_like" class="headerlink" title="Permanent link">¤</a></h3>
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">ones_like</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n"><a class="autorefs autorefs-internal" title="&lt;code class=&quot;doc-symbol doc-symbol-heading doc-symbol-class&quot;&gt;&lt;/code&gt; &lt;span class=&quot;doc doc-object-name doc-class-name&quot;&gt;Tensor&lt;/span&gt; (&lt;code&gt;tinygrad.tensor.Tensor&lt;/code&gt;)" href="../#tinygrad.Tensor">Tensor</a></span>
</code></pre></div>
<div class="doc doc-contents first">
<p>Creates a tensor with the same shape as <code class="language-python highlight"><span class="bp">self</span></code>, filled with ones.</p>
<p>You can pass in <code class="language-python highlight"><span class="n">dtype</span></code> and <code class="language-python highlight"><span class="n">device</span></code> keyword arguments to control the data type and device of the tensor.
Additionally, all other keyword arguments are passed to the constructor of the tensor.</p>
<div class="language-python highlight"><pre><span></span><code><span class="n">t</span> <span class="o">=</span> <span class="n">Tensor</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">Tensor</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">t</span><span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
</code></pre></div>
<div class="language-python highlight"><pre><span></span><code><span class="p">[[</span><span class="mf">1.</span> <span class="mf">1.</span> <span class="mf">1.</span><span class="p">]</span>
<span class="p">[</span><span class="mf">1.</span> <span class="mf">1.</span> <span class="mf">1.</span><span class="p">]]</span>
</code></pre></div>
<details class="mkdocstrings-source">
<summary>Source code in <code>tinygrad/tensor.py</code></summary>
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">775</span>
<span class="normal">776</span>
<span class="normal">777</span>
<span class="normal">778</span>
<span class="normal">779</span>
<span class="normal">780</span>
<span class="normal">781</span>
<span class="normal">782</span>
<span class="normal">783</span>
<span class="normal">784</span>
<span class="normal">785</span>
<span class="normal">786</span>
<span class="normal">787</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span><span class="w"> </span><span class="nf">ones_like</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tensor</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Creates a tensor with the same shape as `self`, filled with ones.</span>
<span class="sd"> You can pass in `dtype` and `device` keyword arguments to control the data type and device of the tensor.</span>
<span class="sd"> Additionally, all other keyword arguments are passed to the constructor of the tensor.</span>
<span class="sd"> ```python exec=&quot;true&quot; source=&quot;above&quot; session=&quot;tensor&quot; result=&quot;python&quot;</span>
<span class="sd"> t = Tensor.zeros(2, 3)</span>
<span class="sd"> print(Tensor.ones_like(t).numpy())</span>
<span class="sd"> ```</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">full_like</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
</code></pre></div></td></tr></table></div>
</details>
</div>
</div><h2 id="creation-external">Creation (external)<a class="headerlink" href="#creation-external" title="Permanent link">¤</a></h2>
<div class="doc doc-object doc-function">
<h3 id="tinygrad.Tensor.from_blob" class="doc doc-heading">
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">from_blob</span>
<span class="doc doc-labels">
<small class="doc doc-label doc-label-staticmethod"><code>staticmethod</code></small>
</span>
<a href="#tinygrad.Tensor.from_blob" class="headerlink" title="Permanent link">¤</a></h3>
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">from_blob</span><span class="p">(</span>
<span class="n">ptr</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-internal" title="&lt;code class=&quot;doc-symbol doc-symbol-heading doc-symbol-method&quot;&gt;&lt;/code&gt; &lt;span class=&quot;doc doc-object-name doc-function-name&quot;&gt;int&lt;/span&gt; (&lt;code&gt;tinygrad.tensor.Tensor.int&lt;/code&gt;)" href="../elementwise/#tinygrad.Tensor.int">int</a></span><span class="p">,</span> <span class="n">shape</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/stdtypes.html#tuple">tuple</a></span><span class="p">[</span><span class="n"><a class="autorefs autorefs-internal" title="&lt;code class=&quot;doc-symbol doc-symbol-heading doc-symbol-method&quot;&gt;&lt;/code&gt; &lt;span class=&quot;doc doc-object-name doc-function-name&quot;&gt;int&lt;/span&gt; (&lt;code&gt;tinygrad.tensor.Tensor.int&lt;/code&gt;)" href="../elementwise/#tinygrad.Tensor.int">int</a></span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="o">**</span><span class="n">kwargs</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n"><a class="autorefs autorefs-internal" title="&lt;code class=&quot;doc-symbol doc-symbol-heading doc-symbol-class&quot;&gt;&lt;/code&gt; &lt;span class=&quot;doc doc-object-name doc-class-name&quot;&gt;Tensor&lt;/span&gt; (&lt;code&gt;tinygrad.tensor.Tensor&lt;/code&gt;)" href="../#tinygrad.Tensor">Tensor</a></span>
</code></pre></div>
<div class="doc doc-contents first">
<p>Exposes the pointer as a Tensor without taking ownership of the original data.
The pointer must remain valid for the entire lifetime of the created Tensor.</p>
<p>You can pass in <code class="language-python highlight"><span class="n">dtype</span></code> and <code class="language-python highlight"><span class="n">device</span></code> keyword arguments to control the data type and device of the tensor.
Additionally, all other keyword arguments are passed to the constructor of the tensor.</p>
<details class="mkdocstrings-source">
<summary>Source code in <code>tinygrad/tensor.py</code></summary>
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">508</span>
<span class="normal">509</span>
<span class="normal">510</span>
<span class="normal">511</span>
<span class="normal">512</span>
<span class="normal">513</span>
<span class="normal">514</span>
<span class="normal">515</span>
<span class="normal">516</span>
<span class="normal">517</span>
<span class="normal">518</span>
<span class="normal">519</span>
<span class="normal">520</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="nd">@staticmethod</span>
<span class="k">def</span><span class="w"> </span><span class="nf">from_blob</span><span class="p">(</span><span class="n">ptr</span><span class="p">:</span><span class="nb">int</span><span class="p">,</span> <span class="n">shape</span><span class="p">:</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tensor</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Exposes the pointer as a Tensor without taking ownership of the original data.</span>
<span class="sd"> The pointer must remain valid for the entire lifetime of the created Tensor.</span>
<span class="sd"> You can pass in `dtype` and `device` keyword arguments to control the data type and device of the tensor.</span>
<span class="sd"> Additionally, all other keyword arguments are passed to the constructor of the tensor.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">r</span> <span class="o">=</span> <span class="n">Tensor</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">r</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span>
<span class="n">cast</span><span class="p">(</span><span class="n">Buffer</span><span class="p">,</span> <span class="n">r</span><span class="o">.</span><span class="n">uop</span><span class="o">.</span><span class="n">buffer</span><span class="p">)</span><span class="o">.</span><span class="n">allocate</span><span class="p">(</span><span class="n">external_ptr</span><span class="o">=</span><span class="n">ptr</span><span class="p">)</span>
<span class="k">return</span> <span class="n">r</span>
</code></pre></div></td></tr></table></div>
</details>
</div>
</div>
<div class="doc doc-object doc-function">
<h3 id="tinygrad.Tensor.from_url" class="doc doc-heading">
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">from_url</span>
<span class="doc doc-labels">
<small class="doc doc-label doc-label-staticmethod"><code>staticmethod</code></small>
</span>
<a href="#tinygrad.Tensor.from_url" class="headerlink" title="Permanent link">¤</a></h3>
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">from_url</span><span class="p">(</span>
<span class="n">url</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/stdtypes.html#str">str</a></span><span class="p">,</span> <span class="n">gunzip</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-internal" title="&lt;code class=&quot;doc-symbol doc-symbol-heading doc-symbol-method&quot;&gt;&lt;/code&gt; &lt;span class=&quot;doc doc-object-name doc-function-name&quot;&gt;bool&lt;/span&gt; (&lt;code&gt;tinygrad.tensor.Tensor.bool&lt;/code&gt;)" href="../elementwise/#tinygrad.Tensor.bool">bool</a></span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n"><a class="autorefs autorefs-internal" title="&lt;code class=&quot;doc-symbol doc-symbol-heading doc-symbol-class&quot;&gt;&lt;/code&gt; &lt;span class=&quot;doc doc-object-name doc-class-name&quot;&gt;Tensor&lt;/span&gt; (&lt;code&gt;tinygrad.tensor.Tensor&lt;/code&gt;)" href="../#tinygrad.Tensor">Tensor</a></span>
</code></pre></div>
<div class="doc doc-contents first">
<p>Creates a Tensor from a URL.</p>
<p>This is the preferred way to access Internet resources.
It currently returns a DISK Tensor, but in the future it may return an HTTP Tensor.
This also will soon become lazy (when possible) and not print progress without DEBUG.</p>
<p>The <code class="language-python highlight"><span class="n">gunzip</span></code> flag will gzip extract the resource and return an extracted Tensor.</p>
<details class="mkdocstrings-source">
<summary>Source code in <code>tinygrad/tensor.py</code></summary>
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">522</span>
<span class="normal">523</span>
<span class="normal">524</span>
<span class="normal">525</span>
<span class="normal">526</span>
<span class="normal">527</span>
<span class="normal">528</span>
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<span class="normal">530</span>
<span class="normal">531</span>
<span class="normal">532</span>
<span class="normal">533</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="nd">@staticmethod</span>
<span class="k">def</span><span class="w"> </span><span class="nf">from_url</span><span class="p">(</span><span class="n">url</span><span class="p">:</span><span class="nb">str</span><span class="p">,</span> <span class="n">gunzip</span><span class="p">:</span><span class="nb">bool</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tensor</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Creates a Tensor from a URL.</span>
<span class="sd"> This is the preferred way to access Internet resources.</span>
<span class="sd"> It currently returns a DISK Tensor, but in the future it may return an HTTP Tensor.</span>
<span class="sd"> This also will soon become lazy (when possible) and not print progress without DEBUG.</span>
<span class="sd"> The `gunzip` flag will gzip extract the resource and return an extracted Tensor.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="n">Tensor</span><span class="p">(</span><span class="n">fetch</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">gunzip</span><span class="o">=</span><span class="n">gunzip</span><span class="p">),</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
</code></pre></div></td></tr></table></div>
</details>
</div>
</div><h2 id="creation-random">Creation (random)<a class="headerlink" href="#creation-random" title="Permanent link">¤</a></h2>
<div class="doc doc-object doc-function">
<h3 id="tinygrad.Tensor.manual_seed" class="doc doc-heading">
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">manual_seed</span>
<span class="doc doc-labels">
<small class="doc doc-label doc-label-staticmethod"><code>staticmethod</code></small>
</span>
<a href="#tinygrad.Tensor.manual_seed" class="headerlink" title="Permanent link">¤</a></h3>
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">manual_seed</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span>
</code></pre></div>
<div class="doc doc-contents first">
<p>Sets the seed for random operations.</p>
<p><div class="language-python highlight"><pre><span></span><code><span class="n">Tensor</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">Tensor</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
<span class="nb">print</span><span class="p">(</span><span class="n">Tensor</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
</code></pre></div>
<div class="language-python highlight"><pre><span></span><code><span class="p">[</span><span class="mf">0.997</span> <span class="mf">0.5899</span> <span class="mf">0.2225</span> <span class="mf">0.7551</span> <span class="mf">0.9057</span><span class="p">]</span>
<span class="p">[</span><span class="mf">0.6162</span> <span class="mf">0.6213</span> <span class="mf">0.9791</span> <span class="mf">0.7851</span> <span class="mf">0.4178</span><span class="p">]</span>
</code></pre></div>
<div class="language-python highlight"><pre><span></span><code><span class="n">Tensor</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span> <span class="c1"># reset to the same seed</span>
<span class="nb">print</span><span class="p">(</span><span class="n">Tensor</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
<span class="nb">print</span><span class="p">(</span><span class="n">Tensor</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
</code></pre></div>
<div class="language-python highlight"><pre><span></span><code><span class="p">[</span><span class="mf">0.997</span> <span class="mf">0.5899</span> <span class="mf">0.2225</span> <span class="mf">0.7551</span> <span class="mf">0.9057</span><span class="p">]</span>
<span class="p">[</span><span class="mf">0.6162</span> <span class="mf">0.6213</span> <span class="mf">0.9791</span> <span class="mf">0.7851</span> <span class="mf">0.4178</span><span class="p">]</span>
</code></pre></div></p>
<details class="mkdocstrings-source">
<summary>Source code in <code>tinygrad/tensor.py</code></summary>
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">538</span>
<span class="normal">539</span>
<span class="normal">540</span>
<span class="normal">541</span>
<span class="normal">542</span>
<span class="normal">543</span>
<span class="normal">544</span>
<span class="normal">545</span>
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<span class="normal">547</span>
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<span class="normal">553</span>
<span class="normal">554</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="nd">@staticmethod</span>
<span class="k">def</span><span class="w"> </span><span class="nf">manual_seed</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Sets the seed for random operations.</span>
<span class="sd"> ```python exec=&quot;true&quot; source=&quot;above&quot; session=&quot;tensor&quot; result=&quot;python&quot;</span>
<span class="sd"> Tensor.manual_seed(42)</span>
<span class="sd"> print(Tensor.rand(5).numpy())</span>
<span class="sd"> print(Tensor.rand(5).numpy())</span>
<span class="sd"> ```</span>
<span class="sd"> ```python exec=&quot;true&quot; source=&quot;above&quot; session=&quot;tensor&quot; result=&quot;python&quot;</span>
<span class="sd"> Tensor.manual_seed(42) # reset to the same seed</span>
<span class="sd"> print(Tensor.rand(5).numpy())</span>
<span class="sd"> print(Tensor.rand(5).numpy())</span>
<span class="sd"> ```</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">Tensor</span><span class="o">.</span><span class="n">_seed</span><span class="p">,</span> <span class="n">Tensor</span><span class="o">.</span><span class="n">_device_seeds</span><span class="p">,</span> <span class="n">Tensor</span><span class="o">.</span><span class="n">_device_rng_counters</span> <span class="o">=</span> <span class="n">seed</span><span class="p">,</span> <span class="p">{},</span> <span class="p">{}</span>
</code></pre></div></td></tr></table></div>
</details>
</div>
</div>
<div class="doc doc-object doc-function">
<h3 id="tinygrad.Tensor.rand" class="doc doc-heading">
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">rand</span>
<span class="doc doc-labels">
<small class="doc doc-label doc-label-staticmethod"><code>staticmethod</code></small>
</span>
<a href="#tinygrad.Tensor.rand" class="headerlink" title="Permanent link">¤</a></h3>
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">rand</span><span class="p">(</span>
<span class="o">*</span><span class="n">shape</span><span class="p">,</span>
<span class="n">device</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/stdtypes.html#str">str</a></span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">dtype</span><span class="p">:</span> <span class="n"><span title="tinygrad.dtype.DTypeLike">DTypeLike</span></span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">contiguous</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-internal" title="&lt;code class=&quot;doc-symbol doc-symbol-heading doc-symbol-method&quot;&gt;&lt;/code&gt; &lt;span class=&quot;doc doc-object-name doc-function-name&quot;&gt;bool&lt;/span&gt; (&lt;code&gt;tinygrad.tensor.Tensor.bool&lt;/code&gt;)" href="../elementwise/#tinygrad.Tensor.bool">bool</a></span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
<span class="o">**</span><span class="n">kwargs</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n"><a class="autorefs autorefs-internal" title="&lt;code class=&quot;doc-symbol doc-symbol-heading doc-symbol-class&quot;&gt;&lt;/code&gt; &lt;span class=&quot;doc doc-object-name doc-class-name&quot;&gt;Tensor&lt;/span&gt; (&lt;code&gt;tinygrad.tensor.Tensor&lt;/code&gt;)" href="../#tinygrad.Tensor">Tensor</a></span>
</code></pre></div>
<div class="doc doc-contents first">
<p>Creates a tensor with the given shape, filled with random values from a uniform distribution over the interval <code class="language-python highlight"><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span></code>.</p>
<p>You can pass in <code class="language-python highlight"><span class="n">dtype</span></code> and <code class="language-python highlight"><span class="n">device</span></code> keyword arguments to control the data type and device of the tensor.
Additionally, all other keyword arguments are passed to the constructor of the tensor.</p>
<div class="language-python highlight"><pre><span></span><code><span class="n">Tensor</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
<span class="n">t</span> <span class="o">=</span> <span class="n">Tensor</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">t</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
</code></pre></div>
<div class="language-python highlight"><pre><span></span><code><span class="p">[[</span><span class="mf">0.997</span> <span class="mf">0.5899</span> <span class="mf">0.2225</span><span class="p">]</span>
<span class="p">[</span><span class="mf">0.7551</span> <span class="mf">0.9057</span> <span class="mf">0.8649</span><span class="p">]]</span>
</code></pre></div>
<details class="mkdocstrings-source">
<summary>Source code in <code>tinygrad/tensor.py</code></summary>
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">563</span>
<span class="normal">564</span>
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<span class="normal">609</span>
<span class="normal">610</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="nd">@staticmethod</span>
<span class="k">def</span><span class="w"> </span><span class="nf">rand</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="n">device</span><span class="p">:</span><span class="nb">str</span><span class="o">|</span><span class="kc">None</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dtype</span><span class="p">:</span><span class="n">DTypeLike</span><span class="o">|</span><span class="kc">None</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">contiguous</span><span class="p">:</span><span class="nb">bool</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tensor</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Creates a tensor with the given shape, filled with random values from a uniform distribution over the interval `[0, 1)`.</span>
<span class="sd"> You can pass in `dtype` and `device` keyword arguments to control the data type and device of the tensor.</span>
<span class="sd"> Additionally, all other keyword arguments are passed to the constructor of the tensor.</span>
<span class="sd"> ```python exec=&quot;true&quot; source=&quot;above&quot; session=&quot;tensor&quot; result=&quot;python&quot;</span>
<span class="sd"> Tensor.manual_seed(42)</span>
<span class="sd"> t = Tensor.rand(2, 3)</span>
<span class="sd"> print(t.numpy())</span>
<span class="sd"> ```</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">dtypes</span><span class="o">.</span><span class="n">is_float</span><span class="p">(</span><span class="n">dtype</span> <span class="o">:=</span> <span class="n">to_dtype</span><span class="p">(</span><span class="n">dtype</span> <span class="ow">or</span> <span class="n">dtypes</span><span class="o">.</span><span class="n">default_float</span><span class="p">)):</span> <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;rand only supports float dtypes, got </span><span class="si">{</span><span class="n">dtype</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">all_int</span><span class="p">(</span><span class="n">shape</span><span class="o">:=</span><span class="n">argfix</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">))</span> <span class="ow">or</span> <span class="ow">not</span> <span class="nb">all</span><span class="p">(</span><span class="n">s</span> <span class="o">&gt;=</span> <span class="mi">0</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">shape</span><span class="p">):</span> <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;invalid input </span><span class="si">{</span><span class="n">shape</span><span class="si">=}</span><span class="s2">&quot;</span><span class="p">)</span>
<span class="k">if</span> <span class="n">device</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">device</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span> <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;rand only supports single device, got </span><span class="si">{</span><span class="n">device</span><span class="si">=}</span><span class="s2">&quot;</span><span class="p">)</span>
<span class="n">device</span> <span class="o">=</span> <span class="n">canonicalize_device</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
<span class="c1"># if shape has 0, return zero tensor</span>
<span class="k">if</span> <span class="p">(</span><span class="n">numel</span> <span class="o">:=</span> <span class="n">prod</span><span class="p">(</span><span class="n">shape</span><span class="p">))</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> <span class="k">return</span> <span class="n">Tensor</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="n">num</span> <span class="o">=</span> <span class="n">ceildiv</span><span class="p">(</span><span class="n">numel</span> <span class="o">*</span> <span class="n">dtype</span><span class="o">.</span><span class="n">itemsize</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
<span class="c1"># generate per device seeds and rng counter if we haven&#39;t seen this device yet</span>
<span class="k">if</span> <span class="n">device</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">Tensor</span><span class="o">.</span><span class="n">_device_seeds</span><span class="p">:</span>
<span class="n">Tensor</span><span class="o">.</span><span class="n">_device_seeds</span><span class="p">[</span><span class="n">device</span><span class="p">]</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">(</span>
<span class="p">[</span><span class="nb">int</span><span class="o">.</span><span class="n">from_bytes</span><span class="p">(</span><span class="n">hashlib</span><span class="o">.</span><span class="n">sha256</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">Tensor</span><span class="o">.</span><span class="n">_device_seeds</span><span class="p">)</span><span class="o">.</span><span class="n">to_bytes</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="s2">&quot;big&quot;</span><span class="p">))</span><span class="o">.</span><span class="n">digest</span><span class="p">(),</span> <span class="s2">&quot;big&quot;</span><span class="p">),</span> <span class="n">Tensor</span><span class="o">.</span><span class="n">_seed</span><span class="p">],</span>
<span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtypes</span><span class="o">.</span><span class="n">uint32</span><span class="p">,</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">Tensor</span><span class="o">.</span><span class="n">_device_rng_counters</span><span class="p">[</span><span class="n">device</span><span class="p">]</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">([</span><span class="n">num</span><span class="p">],</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtypes</span><span class="o">.</span><span class="n">uint32</span><span class="p">,</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="c1"># increment rng counter for devices</span>
<span class="k">else</span><span class="p">:</span> <span class="n">Tensor</span><span class="o">.</span><span class="n">_device_rng_counters</span><span class="p">[</span><span class="n">device</span><span class="p">]</span><span class="o">.</span><span class="n">assign</span><span class="p">(</span><span class="n">Tensor</span><span class="o">.</span><span class="n">_device_rng_counters</span><span class="p">[</span><span class="n">device</span><span class="p">]</span> <span class="o">+</span> <span class="n">num</span><span class="p">)</span>
<span class="c1"># threefry random bits</span>
<span class="n">bits_count</span> <span class="o">=</span> <span class="n">Tensor</span><span class="o">.</span><span class="n">_device_rng_counters</span><span class="p">[</span><span class="n">device</span><span class="p">]</span> <span class="o">-</span> <span class="n">num</span>
<span class="n">counts0</span> <span class="o">=</span> <span class="p">(</span><span class="n">Tensor</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">ceildiv</span><span class="p">(</span><span class="n">num</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtypes</span><span class="o">.</span><span class="n">uint32</span><span class="p">,</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">+</span><span class="n">bits_count</span><span class="p">)</span>
<span class="n">counts1</span> <span class="o">=</span> <span class="n">counts0</span> <span class="o">+</span> <span class="n">ceildiv</span><span class="p">(</span><span class="n">num</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<span class="n">bits</span> <span class="o">=</span> <span class="n">Tensor</span><span class="o">.</span><span class="n">_threefry_random_bits</span><span class="p">(</span><span class="n">Tensor</span><span class="o">.</span><span class="n">_device_seeds</span><span class="p">[</span><span class="n">device</span><span class="p">],</span> <span class="n">counts0</span><span class="p">,</span> <span class="n">counts1</span><span class="p">)[:</span><span class="n">num</span><span class="p">]</span>
<span class="c1"># bitcast to uint with same number of bits</span>
<span class="n">_</span><span class="p">,</span> <span class="n">nmant</span> <span class="o">=</span> <span class="n">dtypes</span><span class="o">.</span><span class="n">finfo</span><span class="p">(</span><span class="n">dtype</span><span class="p">)</span>
<span class="n">uint_dtype</span> <span class="o">=</span> <span class="p">{</span><span class="mi">1</span><span class="p">:</span> <span class="n">dtypes</span><span class="o">.</span><span class="n">uint8</span><span class="p">,</span> <span class="mi">2</span><span class="p">:</span> <span class="n">dtypes</span><span class="o">.</span><span class="n">uint16</span><span class="p">,</span> <span class="mi">4</span><span class="p">:</span> <span class="n">dtypes</span><span class="o">.</span><span class="n">uint32</span><span class="p">,</span> <span class="mi">8</span><span class="p">:</span> <span class="n">dtypes</span><span class="o">.</span><span class="n">uint64</span><span class="p">}[</span><span class="n">dtype</span><span class="o">.</span><span class="n">itemsize</span><span class="p">]</span>
<span class="n">bits</span> <span class="o">=</span> <span class="n">bits</span><span class="o">.</span><span class="n">bitcast</span><span class="p">(</span><span class="n">uint_dtype</span><span class="p">)</span>
<span class="c1"># only randomize the mantissa bits and set the exponent to 1</span>
<span class="n">one</span> <span class="o">=</span> <span class="n">Tensor</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">bits</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">bits</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span><span class="o">.</span><span class="n">bitcast</span><span class="p">(</span><span class="n">uint_dtype</span><span class="p">)</span>
<span class="n">bits</span> <span class="o">=</span> <span class="n">bits</span><span class="o">.</span><span class="n">rshift</span><span class="p">(</span><span class="n">dtype</span><span class="o">.</span><span class="n">bitsize</span> <span class="o">-</span> <span class="n">nmant</span><span class="p">)</span><span class="o">.</span><span class="n">bitwise_or</span><span class="p">(</span><span class="n">one</span><span class="p">)</span>
<span class="c1"># bitcast back to the original dtype and reshape</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">bits</span><span class="o">.</span><span class="n">bitcast</span><span class="p">(</span><span class="n">dtype</span><span class="p">)[:</span><span class="n">numel</span><span class="p">]</span><span class="o">.</span><span class="n">sub</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span><span class="o">.</span><span class="n">requires_grad_</span><span class="p">(</span><span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;requires_grad&quot;</span><span class="p">))</span>
<span class="k">return</span> <span class="n">out</span><span class="o">.</span><span class="n">contiguous</span><span class="p">()</span> <span class="k">if</span> <span class="n">contiguous</span> <span class="k">else</span> <span class="n">out</span>
</code></pre></div></td></tr></table></div>
</details>
</div>
</div>
<div class="doc doc-object doc-function">
<h3 id="tinygrad.Tensor.rand_like" class="doc doc-heading">
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">rand_like</span>
<a href="#tinygrad.Tensor.rand_like" class="headerlink" title="Permanent link">¤</a></h3>
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">rand_like</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n"><a class="autorefs autorefs-internal" title="&lt;code class=&quot;doc-symbol doc-symbol-heading doc-symbol-class&quot;&gt;&lt;/code&gt; &lt;span class=&quot;doc doc-object-name doc-class-name&quot;&gt;Tensor&lt;/span&gt; (&lt;code&gt;tinygrad.tensor.Tensor&lt;/code&gt;)" href="../#tinygrad.Tensor">Tensor</a></span>
</code></pre></div>
<div class="doc doc-contents first">
<p>Creates a tensor with the same shape and sharding as <code class="language-python highlight"><span class="bp">self</span></code>, filled with random values from a uniform distribution over the interval <code class="language-python highlight"><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span></code>.</p>
<p>You can pass in <code class="language-python highlight"><span class="n">dtype</span></code> and <code class="language-python highlight"><span class="n">device</span></code> keyword arguments to control the data type and device of the tensor.
Additionally, all other keyword arguments are passed to the constructor of the tensor.</p>
<div class="language-python highlight"><pre><span></span><code><span class="n">t</span> <span class="o">=</span> <span class="n">Tensor</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">Tensor</span><span class="o">.</span><span class="n">rand_like</span><span class="p">(</span><span class="n">t</span><span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
</code></pre></div>
<div class="language-python highlight"><pre><span></span><code><span class="p">[[</span><span class="mf">0.6213</span> <span class="mf">0.9791</span> <span class="mf">0.8408</span><span class="p">]</span>
<span class="p">[</span><span class="mf">0.4178</span> <span class="mf">0.6334</span> <span class="mf">0.9325</span><span class="p">]]</span>
</code></pre></div>
<details class="mkdocstrings-source">
<summary>Source code in <code>tinygrad/tensor.py</code></summary>
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">789</span>
<span class="normal">790</span>
<span class="normal">791</span>
<span class="normal">792</span>
<span class="normal">793</span>
<span class="normal">794</span>
<span class="normal">795</span>
<span class="normal">796</span>
<span class="normal">797</span>
<span class="normal">798</span>
<span class="normal">799</span>
<span class="normal">800</span>
<span class="normal">801</span>
<span class="normal">802</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span><span class="w"> </span><span class="nf">rand_like</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tensor</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Creates a tensor with the same shape and sharding as `self`, filled with random values from a uniform distribution over the interval `[0, 1)`.</span>
<span class="sd"> You can pass in `dtype` and `device` keyword arguments to control the data type and device of the tensor.</span>
<span class="sd"> Additionally, all other keyword arguments are passed to the constructor of the tensor.</span>
<span class="sd"> ```python exec=&quot;true&quot; source=&quot;above&quot; session=&quot;tensor&quot; result=&quot;python&quot;</span>
<span class="sd"> t = Tensor.ones(2, 3)</span>
<span class="sd"> print(Tensor.rand_like(t).numpy())</span>
<span class="sd"> ```</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">):</span> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_multi_like</span><span class="p">(</span><span class="n">Tensor</span><span class="o">.</span><span class="n">rand</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="k">return</span> <span class="n">Tensor</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">&quot;device&quot;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">&quot;dtype&quot;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">),</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
</code></pre></div></td></tr></table></div>
</details>
</div>
</div>
<div class="doc doc-object doc-function">
<h3 id="tinygrad.Tensor.randn" class="doc doc-heading">
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">randn</span>
<span class="doc doc-labels">
<small class="doc doc-label doc-label-staticmethod"><code>staticmethod</code></small>
</span>
<a href="#tinygrad.Tensor.randn" class="headerlink" title="Permanent link">¤</a></h3>
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">randn</span><span class="p">(</span>
<span class="o">*</span><span class="n">shape</span><span class="p">,</span>
<span class="n">dtype</span><span class="p">:</span> <span class="n"><span title="tinygrad.dtype.DTypeLike">DTypeLike</span></span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">requires_grad</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-internal" title="&lt;code class=&quot;doc-symbol doc-symbol-heading doc-symbol-method&quot;&gt;&lt;/code&gt; &lt;span class=&quot;doc doc-object-name doc-function-name&quot;&gt;bool&lt;/span&gt; (&lt;code&gt;tinygrad.tensor.Tensor.bool&lt;/code&gt;)" href="../elementwise/#tinygrad.Tensor.bool">bool</a></span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="o">**</span><span class="n">kwargs</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n"><a class="autorefs autorefs-internal" title="&lt;code class=&quot;doc-symbol doc-symbol-heading doc-symbol-class&quot;&gt;&lt;/code&gt; &lt;span class=&quot;doc doc-object-name doc-class-name&quot;&gt;Tensor&lt;/span&gt; (&lt;code&gt;tinygrad.tensor.Tensor&lt;/code&gt;)" href="../#tinygrad.Tensor">Tensor</a></span>
</code></pre></div>
<div class="doc doc-contents first">
<p>Creates a tensor with the given shape, filled with random values from a normal distribution with mean <code class="language-python highlight"><span class="mi">0</span></code> and standard deviation <code class="language-python highlight"><span class="mi">1</span></code>.
If <code class="language-python highlight"><span class="n">dtype</span></code> is not specified, the default type is used.</p>
<p>You can pass in the <code class="language-python highlight"><span class="n">device</span></code> keyword argument to control device of the tensor.
Additionally, all other keyword arguments are passed to the constructor of the tensor.</p>
<div class="language-python highlight"><pre><span></span><code><span class="n">Tensor</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">Tensor</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
</code></pre></div>
<div class="language-python highlight"><pre><span></span><code><span class="p">[[</span> <span class="mf">0.9779</span> <span class="mf">0.4678</span> <span class="mf">0.5526</span><span class="p">]</span>
<span class="p">[</span><span class="o">-</span><span class="mf">0.3288</span> <span class="o">-</span><span class="mf">0.8555</span> <span class="mf">0.2753</span><span class="p">]]</span>
</code></pre></div>
<details class="mkdocstrings-source">
<summary>Source code in <code>tinygrad/tensor.py</code></summary>
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">822</span>
<span class="normal">823</span>
<span class="normal">824</span>
<span class="normal">825</span>
<span class="normal">826</span>
<span class="normal">827</span>
<span class="normal">828</span>
<span class="normal">829</span>
<span class="normal">830</span>
<span class="normal">831</span>
<span class="normal">832</span>
<span class="normal">833</span>
<span class="normal">834</span>
<span class="normal">835</span>
<span class="normal">836</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="nd">@staticmethod</span>
<span class="k">def</span><span class="w"> </span><span class="nf">randn</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="p">:</span><span class="n">DTypeLike</span><span class="o">|</span><span class="kc">None</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">requires_grad</span><span class="p">:</span><span class="nb">bool</span><span class="o">|</span><span class="kc">None</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tensor</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Creates a tensor with the given shape, filled with random values from a normal distribution with mean `0` and standard deviation `1`.</span>
<span class="sd"> If `dtype` is not specified, the default type is used.</span>
<span class="sd"> You can pass in the `device` keyword argument to control device of the tensor.</span>
<span class="sd"> Additionally, all other keyword arguments are passed to the constructor of the tensor.</span>
<span class="sd"> ```python exec=&quot;true&quot; source=&quot;above&quot; session=&quot;tensor&quot; result=&quot;python&quot;</span>
<span class="sd"> Tensor.manual_seed(42)</span>
<span class="sd"> print(Tensor.randn(2, 3).numpy())</span>
<span class="sd"> ```</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="n">Tensor</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span><span class="o">.</span><span class="n">randn_like</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span> <span class="n">requires_grad</span><span class="o">=</span><span class="n">requires_grad</span><span class="p">)</span>
</code></pre></div></td></tr></table></div>
</details>
</div>
</div>
<div class="doc doc-object doc-function">
<h3 id="tinygrad.Tensor.randn_like" class="doc doc-heading">
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">randn_like</span>
<a href="#tinygrad.Tensor.randn_like" class="headerlink" title="Permanent link">¤</a></h3>
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">randn_like</span><span class="p">(</span>
<span class="n">dtype</span><span class="p">:</span> <span class="n"><span title="tinygrad.dtype.DTypeLike">DTypeLike</span></span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">requires_grad</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-internal" title="&lt;code class=&quot;doc-symbol doc-symbol-heading doc-symbol-method&quot;&gt;&lt;/code&gt; &lt;span class=&quot;doc doc-object-name doc-function-name&quot;&gt;bool&lt;/span&gt; (&lt;code&gt;tinygrad.tensor.Tensor.bool&lt;/code&gt;)" href="../elementwise/#tinygrad.Tensor.bool">bool</a></span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="o">**</span><span class="n">kwargs</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n"><a class="autorefs autorefs-internal" title="&lt;code class=&quot;doc-symbol doc-symbol-heading doc-symbol-class&quot;&gt;&lt;/code&gt; &lt;span class=&quot;doc doc-object-name doc-class-name&quot;&gt;Tensor&lt;/span&gt; (&lt;code&gt;tinygrad.tensor.Tensor&lt;/code&gt;)" href="../#tinygrad.Tensor">Tensor</a></span>
</code></pre></div>
<div class="doc doc-contents first">
<p>Creates a tensor with the same shape and sharding as <code class="language-python highlight"><span class="bp">self</span></code>, filled with random values from a normal distribution with mean 0 and variance 1.</p>
<p>You can pass in <code class="language-python highlight"><span class="n">dtype</span></code> and <code class="language-python highlight"><span class="n">device</span></code> keyword arguments to control the data type and device of the tensor.
Additionally, all other keyword arguments are passed to the constructor of the tensor.</p>
<div class="language-python highlight"><pre><span></span><code><span class="n">t</span> <span class="o">=</span> <span class="n">Tensor</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">Tensor</span><span class="o">.</span><span class="n">randn_like</span><span class="p">(</span><span class="n">t</span><span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
</code></pre></div>
<div class="language-python highlight"><pre><span></span><code><span class="p">[[</span> <span class="mf">0.0229</span> <span class="o">-</span><span class="mf">0.8954</span> <span class="mf">0.415</span> <span class="p">]</span>
<span class="p">[</span><span class="o">-</span><span class="mf">1.5933</span> <span class="mf">0.96</span> <span class="o">-</span><span class="mf">1.2354</span><span class="p">]]</span>
</code></pre></div>
<details class="mkdocstrings-source">
<summary>Source code in <code>tinygrad/tensor.py</code></summary>
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">806</span>
<span class="normal">807</span>
<span class="normal">808</span>
<span class="normal">809</span>
<span class="normal">810</span>
<span class="normal">811</span>
<span class="normal">812</span>
<span class="normal">813</span>
<span class="normal">814</span>
<span class="normal">815</span>
<span class="normal">816</span>
<span class="normal">817</span>
<span class="normal">818</span>
<span class="normal">819</span>
<span class="normal">820</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span><span class="w"> </span><span class="nf">randn_like</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dtype</span><span class="p">:</span><span class="n">DTypeLike</span><span class="o">|</span><span class="kc">None</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">requires_grad</span><span class="p">:</span><span class="nb">bool</span><span class="o">|</span><span class="kc">None</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tensor</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Creates a tensor with the same shape and sharding as `self`, filled with random values from a normal distribution with mean 0 and variance 1.</span>
<span class="sd"> You can pass in `dtype` and `device` keyword arguments to control the data type and device of the tensor.</span>
<span class="sd"> Additionally, all other keyword arguments are passed to the constructor of the tensor.</span>
<span class="sd"> ```python exec=&quot;true&quot; source=&quot;above&quot; session=&quot;tensor&quot; result=&quot;python&quot;</span>
<span class="sd"> t = Tensor.ones(2, 3)</span>
<span class="sd"> print(Tensor.randn_like(t).numpy())</span>
<span class="sd"> ```</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">src</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">rand_like</span><span class="p">(</span><span class="o">**</span><span class="p">{</span><span class="o">**</span><span class="n">kwargs</span><span class="p">,</span> <span class="s2">&quot;dtype&quot;</span><span class="p">:</span> <span class="n">dtypes</span><span class="o">.</span><span class="n">float32</span><span class="p">})</span>
<span class="c1"># https://en.wikipedia.org/wiki/Box%E2%80%93Muller_transform</span>
<span class="k">return</span> <span class="p">(</span><span class="n">src</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">mul</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">math</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span><span class="o">.</span><span class="n">cos</span><span class="p">()</span><span class="o">.</span><span class="n">mul</span><span class="p">((</span><span class="mi">1</span> <span class="o">-</span> <span class="n">src</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span><span class="o">.</span><span class="n">log</span><span class="p">()</span><span class="o">.</span><span class="n">mul</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">sqrt</span><span class="p">())</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="n">dtype</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">))</span><span class="o">.</span><span class="n">requires_grad_</span><span class="p">(</span><span class="n">requires_grad</span><span class="p">)</span>
</code></pre></div></td></tr></table></div>
</details>
</div>
</div>
<div class="doc doc-object doc-function">
<h3 id="tinygrad.Tensor.randint" class="doc doc-heading">
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">randint</span>
<span class="doc doc-labels">
<small class="doc doc-label doc-label-staticmethod"><code>staticmethod</code></small>
</span>
<a href="#tinygrad.Tensor.randint" class="headerlink" title="Permanent link">¤</a></h3>
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">randint</span><span class="p">(</span>
<span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="n">low</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">high</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n"><a class="autorefs autorefs-internal" title="&lt;code class=&quot;doc-symbol doc-symbol-heading doc-symbol-attribute&quot;&gt;&lt;/code&gt; &lt;span class=&quot;doc doc-object-name doc-attribute-name&quot;&gt;int32&lt;/span&gt; (&lt;code&gt;tinygrad.dtype.dtypes.int32&lt;/code&gt;)" href="../../dtypes/#tinygrad.dtype.dtypes.int32">int32</a></span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n"><a class="autorefs autorefs-internal" title="&lt;code class=&quot;doc-symbol doc-symbol-heading doc-symbol-class&quot;&gt;&lt;/code&gt; &lt;span class=&quot;doc doc-object-name doc-class-name&quot;&gt;Tensor&lt;/span&gt; (&lt;code&gt;tinygrad.tensor.Tensor&lt;/code&gt;)" href="../#tinygrad.Tensor">Tensor</a></span>
</code></pre></div>
<div class="doc doc-contents first">
<p>Creates a tensor with the given shape, filled with random integer values generated uniformly from the interval <code class="language-python highlight"><span class="p">[</span><span class="n">low</span><span class="p">,</span> <span class="n">high</span><span class="p">)</span></code>.
If <code class="language-python highlight"><span class="n">dtype</span></code> is not specified, the default type is used.</p>
<p>You can pass in the <code class="language-python highlight"><span class="n">device</span></code> keyword argument to control device of the tensor.
Additionally, all other keyword arguments are passed to the constructor of the tensor.</p>
<div class="language-python highlight"><pre><span></span><code><span class="n">Tensor</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">Tensor</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">low</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">high</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
</code></pre></div>
<div class="language-python highlight"><pre><span></span><code><span class="p">[[</span><span class="mi">9</span> <span class="mi">7</span> <span class="mi">6</span><span class="p">]</span>
<span class="p">[</span><span class="mi">8</span> <span class="mi">9</span> <span class="mi">9</span><span class="p">]]</span>
</code></pre></div>
<details class="mkdocstrings-source">
<summary>Source code in <code>tinygrad/tensor.py</code></summary>
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">838</span>
<span class="normal">839</span>
<span class="normal">840</span>
<span class="normal">841</span>
<span class="normal">842</span>
<span class="normal">843</span>
<span class="normal">844</span>
<span class="normal">845</span>
<span class="normal">846</span>
<span class="normal">847</span>
<span class="normal">848</span>
<span class="normal">849</span>
<span class="normal">850</span>
<span class="normal">851</span>
<span class="normal">852</span>
<span class="normal">853</span>
<span class="normal">854</span>
<span class="normal">855</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="nd">@staticmethod</span>
<span class="k">def</span><span class="w"> </span><span class="nf">randint</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="n">low</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">high</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtypes</span><span class="o">.</span><span class="n">int32</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tensor</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Creates a tensor with the given shape, filled with random integer values generated uniformly from the interval `[low, high)`.</span>
<span class="sd"> If `dtype` is not specified, the default type is used.</span>
<span class="sd"> You can pass in the `device` keyword argument to control device of the tensor.</span>
<span class="sd"> Additionally, all other keyword arguments are passed to the constructor of the tensor.</span>
<span class="sd"> ```python exec=&quot;true&quot; source=&quot;above&quot; session=&quot;tensor&quot; result=&quot;python&quot;</span>
<span class="sd"> Tensor.manual_seed(42)</span>
<span class="sd"> print(Tensor.randint(2, 3, low=5, high=10).numpy())</span>
<span class="sd"> ```</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">low</span><span class="p">,</span> <span class="nb">int</span><span class="p">)</span> <span class="ow">or</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">high</span><span class="p">,</span> <span class="nb">int</span><span class="p">):</span> <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">low</span><span class="si">=}</span><span class="s2"> and </span><span class="si">{</span><span class="n">high</span><span class="si">=}</span><span class="s2"> must be integers&quot;</span><span class="p">)</span>
<span class="n">dtype</span> <span class="o">=</span> <span class="n">to_dtype</span><span class="p">(</span><span class="n">dtype</span><span class="p">)</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">dtypes</span><span class="o">.</span><span class="n">is_int</span><span class="p">(</span><span class="n">dtype</span><span class="p">):</span> <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">dtype</span><span class="si">=}</span><span class="s2"> must be int&quot;</span><span class="p">)</span>
<span class="k">return</span> <span class="n">Tensor</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="n">low</span><span class="o">=</span><span class="n">low</span><span class="p">,</span> <span class="n">high</span><span class="o">=</span><span class="n">high</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
</code></pre></div></td></tr></table></div>
</details>
</div>
</div>
<div class="doc doc-object doc-function">
<h3 id="tinygrad.Tensor.randperm" class="doc doc-heading">
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">randperm</span>
<span class="doc doc-labels">
<small class="doc doc-label doc-label-staticmethod"><code>staticmethod</code></small>
</span>
<a href="#tinygrad.Tensor.randperm" class="headerlink" title="Permanent link">¤</a></h3>
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">randperm</span><span class="p">(</span>
<span class="n">n</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-internal" title="&lt;code class=&quot;doc-symbol doc-symbol-heading doc-symbol-method&quot;&gt;&lt;/code&gt; &lt;span class=&quot;doc doc-object-name doc-function-name&quot;&gt;int&lt;/span&gt; (&lt;code&gt;tinygrad.tensor.Tensor.int&lt;/code&gt;)" href="../elementwise/#tinygrad.Tensor.int">int</a></span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n"><a class="autorefs autorefs-internal" title="&lt;code class=&quot;doc-symbol doc-symbol-heading doc-symbol-attribute&quot;&gt;&lt;/code&gt; &lt;span class=&quot;doc doc-object-name doc-attribute-name&quot;&gt;int32&lt;/span&gt; (&lt;code&gt;tinygrad.dtype.dtypes.int32&lt;/code&gt;)" href="../../dtypes/#tinygrad.dtype.dtypes.int32">int32</a></span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n"><a class="autorefs autorefs-internal" title="&lt;code class=&quot;doc-symbol doc-symbol-heading doc-symbol-class&quot;&gt;&lt;/code&gt; &lt;span class=&quot;doc doc-object-name doc-class-name&quot;&gt;Tensor&lt;/span&gt; (&lt;code&gt;tinygrad.tensor.Tensor&lt;/code&gt;)" href="../#tinygrad.Tensor">Tensor</a></span>
</code></pre></div>
<div class="doc doc-contents first">
<p>Returns a tensor with a random permutation of integers from <code class="language-python highlight"><span class="mi">0</span></code> to <code class="language-python highlight"><span class="n">n</span><span class="o">-</span><span class="mi">1</span></code>.</p>
<div class="language-python highlight"><pre><span></span><code><span class="n">Tensor</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">Tensor</span><span class="o">.</span><span class="n">randperm</span><span class="p">(</span><span class="mi">6</span><span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
</code></pre></div>
<div class="language-python highlight"><pre><span></span><code><span class="p">[</span><span class="mi">2</span> <span class="mi">1</span> <span class="mi">3</span> <span class="mi">5</span> <span class="mi">4</span> <span class="mi">0</span><span class="p">]</span>
</code></pre></div>
<details class="mkdocstrings-source">
<summary>Source code in <code>tinygrad/tensor.py</code></summary>
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">953</span>
<span class="normal">954</span>
<span class="normal">955</span>
<span class="normal">956</span>
<span class="normal">957</span>
<span class="normal">958</span>
<span class="normal">959</span>
<span class="normal">960</span>
<span class="normal">961</span>
<span class="normal">962</span>
<span class="normal">963</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="nd">@staticmethod</span>
<span class="k">def</span><span class="w"> </span><span class="nf">randperm</span><span class="p">(</span><span class="n">n</span><span class="p">:</span><span class="nb">int</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtypes</span><span class="o">.</span><span class="n">int32</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tensor</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns a tensor with a random permutation of integers from `0` to `n-1`.</span>
<span class="sd"> ```python exec=&quot;true&quot; source=&quot;above&quot; session=&quot;tensor&quot; result=&quot;python&quot;</span>
<span class="sd"> Tensor.manual_seed(42)</span>
<span class="sd"> print(Tensor.randperm(6).numpy())</span>
<span class="sd"> ```</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="n">Tensor</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span><span class="o">.</span><span class="n">argsort</span><span class="p">()</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="n">dtype</span><span class="p">)</span>
</code></pre></div></td></tr></table></div>
</details>
</div>
</div>
<div class="doc doc-object doc-function">
<h3 id="tinygrad.Tensor.normal" class="doc doc-heading">
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">normal</span>
<span class="doc doc-labels">
<small class="doc doc-label doc-label-staticmethod"><code>staticmethod</code></small>
</span>
<a href="#tinygrad.Tensor.normal" class="headerlink" title="Permanent link">¤</a></h3>
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">normal</span><span class="p">(</span>
<span class="o">*</span><span class="n">shape</span><span class="p">,</span>
<span class="n">mean</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span>
<span class="n">std</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
<span class="n">requires_grad</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-internal" title="&lt;code class=&quot;doc-symbol doc-symbol-heading doc-symbol-method&quot;&gt;&lt;/code&gt; &lt;span class=&quot;doc doc-object-name doc-function-name&quot;&gt;bool&lt;/span&gt; (&lt;code&gt;tinygrad.tensor.Tensor.bool&lt;/code&gt;)" href="../elementwise/#tinygrad.Tensor.bool">bool</a></span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="o">**</span><span class="n">kwargs</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n"><a class="autorefs autorefs-internal" title="&lt;code class=&quot;doc-symbol doc-symbol-heading doc-symbol-class&quot;&gt;&lt;/code&gt; &lt;span class=&quot;doc doc-object-name doc-class-name&quot;&gt;Tensor&lt;/span&gt; (&lt;code&gt;tinygrad.tensor.Tensor&lt;/code&gt;)" href="../#tinygrad.Tensor">Tensor</a></span>
</code></pre></div>
<div class="doc doc-contents first">
<p>Creates a tensor with the given shape, filled with random values from a normal distribution with the given <code class="language-python highlight"><span class="n">mean</span></code> and standard deviation <code class="language-python highlight"><span class="n">std</span></code>.</p>
<p>You can pass in <code class="language-python highlight"><span class="n">dtype</span></code> and <code class="language-python highlight"><span class="n">device</span></code> keyword arguments to control the data type and device of the tensor.
Additionally, all other keyword arguments are passed to the constructor of the tensor.</p>
<div class="language-python highlight"><pre><span></span><code><span class="n">Tensor</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">Tensor</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">mean</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">std</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
</code></pre></div>
<div class="language-python highlight"><pre><span></span><code><span class="p">[[</span><span class="mf">11.9557</span> <span class="mf">10.9356</span> <span class="mf">11.1053</span><span class="p">]</span>
<span class="p">[</span> <span class="mf">9.3423</span> <span class="mf">8.289</span> <span class="mf">10.5505</span><span class="p">]]</span>
</code></pre></div>
<details class="mkdocstrings-source">
<summary>Source code in <code>tinygrad/tensor.py</code></summary>
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">857</span>
<span class="normal">858</span>
<span class="normal">859</span>
<span class="normal">860</span>
<span class="normal">861</span>
<span class="normal">862</span>
<span class="normal">863</span>
<span class="normal">864</span>
<span class="normal">865</span>
<span class="normal">866</span>
<span class="normal">867</span>
<span class="normal">868</span>
<span class="normal">869</span>
<span class="normal">870</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="nd">@staticmethod</span>
<span class="k">def</span><span class="w"> </span><span class="nf">normal</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="n">mean</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">std</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">requires_grad</span><span class="p">:</span><span class="nb">bool</span><span class="o">|</span><span class="kc">None</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tensor</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Creates a tensor with the given shape, filled with random values from a normal distribution with the given `mean` and standard deviation `std`.</span>
<span class="sd"> You can pass in `dtype` and `device` keyword arguments to control the data type and device of the tensor.</span>
<span class="sd"> Additionally, all other keyword arguments are passed to the constructor of the tensor.</span>
<span class="sd"> ```python exec=&quot;true&quot; source=&quot;above&quot; session=&quot;tensor&quot; result=&quot;python&quot;</span>
<span class="sd"> Tensor.manual_seed(42)</span>
<span class="sd"> print(Tensor.normal(2, 3, mean=10, std=2).numpy())</span>
<span class="sd"> ```</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="p">((</span><span class="n">std</span> <span class="o">*</span> <span class="n">Tensor</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">))</span> <span class="o">+</span> <span class="n">mean</span><span class="p">)</span><span class="o">.</span><span class="n">requires_grad_</span><span class="p">(</span><span class="n">requires_grad</span><span class="p">)</span>
</code></pre></div></td></tr></table></div>
</details>
</div>
</div>
<div class="doc doc-object doc-function">
<h3 id="tinygrad.Tensor.uniform" class="doc doc-heading">
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">uniform</span>
<span class="doc doc-labels">
<small class="doc doc-label doc-label-staticmethod"><code>staticmethod</code></small>
</span>
<a href="#tinygrad.Tensor.uniform" class="headerlink" title="Permanent link">¤</a></h3>
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">uniform</span><span class="p">(</span>
<span class="o">*</span><span class="n">shape</span><span class="p">,</span>
<span class="n">low</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span>
<span class="n">high</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
<span class="n">dtype</span><span class="p">:</span> <span class="n"><span title="tinygrad.dtype.DTypeLike">DTypeLike</span></span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="n">requires_grad</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-internal" title="&lt;code class=&quot;doc-symbol doc-symbol-heading doc-symbol-method&quot;&gt;&lt;/code&gt; &lt;span class=&quot;doc doc-object-name doc-function-name&quot;&gt;bool&lt;/span&gt; (&lt;code&gt;tinygrad.tensor.Tensor.bool&lt;/code&gt;)" href="../elementwise/#tinygrad.Tensor.bool">bool</a></span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="o">**</span><span class="n">kwargs</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n"><a class="autorefs autorefs-internal" title="&lt;code class=&quot;doc-symbol doc-symbol-heading doc-symbol-class&quot;&gt;&lt;/code&gt; &lt;span class=&quot;doc doc-object-name doc-class-name&quot;&gt;Tensor&lt;/span&gt; (&lt;code&gt;tinygrad.tensor.Tensor&lt;/code&gt;)" href="../#tinygrad.Tensor">Tensor</a></span>
</code></pre></div>
<div class="doc doc-contents first">
<p>Creates a tensor with the given shape, filled with random values from a uniform distribution over the interval <code class="language-python highlight"><span class="p">[</span><span class="n">low</span><span class="p">,</span> <span class="n">high</span><span class="p">)</span></code>.</p>
<p>You can pass in <code class="language-python highlight"><span class="n">dtype</span></code> and <code class="language-python highlight"><span class="n">device</span></code> keyword arguments to control the data type and device of the tensor.
Additionally, all other keyword arguments are passed to the constructor of the tensor.</p>
<div class="language-python highlight"><pre><span></span><code><span class="n">Tensor</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">Tensor</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">low</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">high</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
</code></pre></div>
<div class="language-python highlight"><pre><span></span><code><span class="p">[[</span><span class="mf">9.9763</span> <span class="mf">6.7193</span> <span class="mf">3.7804</span><span class="p">]</span>
<span class="p">[</span><span class="mf">8.0404</span> <span class="mf">9.2452</span> <span class="mf">8.9191</span><span class="p">]]</span>
</code></pre></div>
<details class="mkdocstrings-source">
<summary>Source code in <code>tinygrad/tensor.py</code></summary>
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">872</span>
<span class="normal">873</span>
<span class="normal">874</span>
<span class="normal">875</span>
<span class="normal">876</span>
<span class="normal">877</span>
<span class="normal">878</span>
<span class="normal">879</span>
<span class="normal">880</span>
<span class="normal">881</span>
<span class="normal">882</span>
<span class="normal">883</span>
<span class="normal">884</span>
<span class="normal">885</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="nd">@staticmethod</span>
<span class="k">def</span><span class="w"> </span><span class="nf">uniform</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="n">low</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">high</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">dtype</span><span class="p">:</span><span class="n">DTypeLike</span><span class="o">|</span><span class="kc">None</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">requires_grad</span><span class="p">:</span><span class="nb">bool</span><span class="o">|</span><span class="kc">None</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tensor</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Creates a tensor with the given shape, filled with random values from a uniform distribution over the interval `[low, high)`.</span>
<span class="sd"> You can pass in `dtype` and `device` keyword arguments to control the data type and device of the tensor.</span>
<span class="sd"> Additionally, all other keyword arguments are passed to the constructor of the tensor.</span>
<span class="sd"> ```python exec=&quot;true&quot; source=&quot;above&quot; session=&quot;tensor&quot; result=&quot;python&quot;</span>
<span class="sd"> Tensor.manual_seed(42)</span>
<span class="sd"> print(Tensor.uniform(2, 3, low=2, high=10).numpy())</span>
<span class="sd"> ```</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="p">(((</span><span class="n">high</span><span class="o">-</span><span class="n">low</span><span class="p">)</span> <span class="o">*</span> <span class="n">Tensor</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">))</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="n">dtype</span> <span class="ow">or</span> <span class="n">dtypes</span><span class="o">.</span><span class="n">default_float</span><span class="p">)</span> <span class="o">+</span> <span class="n">low</span><span class="p">)</span><span class="o">.</span><span class="n">requires_grad_</span><span class="p">(</span><span class="n">requires_grad</span><span class="p">)</span>
</code></pre></div></td></tr></table></div>
</details>
</div>
</div>
<div class="doc doc-object doc-function">
<h3 id="tinygrad.Tensor.scaled_uniform" class="doc doc-heading">
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">scaled_uniform</span>
<span class="doc doc-labels">
<small class="doc doc-label doc-label-staticmethod"><code>staticmethod</code></small>
</span>
<a href="#tinygrad.Tensor.scaled_uniform" class="headerlink" title="Permanent link">¤</a></h3>
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">scaled_uniform</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n"><a class="autorefs autorefs-internal" title="&lt;code class=&quot;doc-symbol doc-symbol-heading doc-symbol-class&quot;&gt;&lt;/code&gt; &lt;span class=&quot;doc doc-object-name doc-class-name&quot;&gt;Tensor&lt;/span&gt; (&lt;code&gt;tinygrad.tensor.Tensor&lt;/code&gt;)" href="../#tinygrad.Tensor">Tensor</a></span>
</code></pre></div>
<div class="doc doc-contents first">
<p>Creates a tensor with the given shape, filled with random values from a uniform distribution
over the interval <code class="language-python highlight"><span class="p">[</span><span class="o">-</span><span class="n">prod</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span><span class="o">**-</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">prod</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span><span class="o">**-</span><span class="mf">0.5</span><span class="p">)</span></code>.</p>
<p>You can pass in <code class="language-python highlight"><span class="n">dtype</span></code> and <code class="language-python highlight"><span class="n">device</span></code> keyword arguments to control the data type and device of the tensor.
Additionally, all other keyword arguments are passed to the constructor of the tensor.</p>
<div class="language-python highlight"><pre><span></span><code><span class="n">Tensor</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">Tensor</span><span class="o">.</span><span class="n">scaled_uniform</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
</code></pre></div>
<div class="language-python highlight"><pre><span></span><code><span class="p">[[</span> <span class="mf">0.4058</span> <span class="mf">0.0734</span> <span class="o">-</span><span class="mf">0.2265</span><span class="p">]</span>
<span class="p">[</span> <span class="mf">0.2082</span> <span class="mf">0.3312</span> <span class="mf">0.2979</span><span class="p">]]</span>
</code></pre></div>
<details class="mkdocstrings-source">
<summary>Source code in <code>tinygrad/tensor.py</code></summary>
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">887</span>
<span class="normal">888</span>
<span class="normal">889</span>
<span class="normal">890</span>
<span class="normal">891</span>
<span class="normal">892</span>
<span class="normal">893</span>
<span class="normal">894</span>
<span class="normal">895</span>
<span class="normal">896</span>
<span class="normal">897</span>
<span class="normal">898</span>
<span class="normal">899</span>
<span class="normal">900</span>
<span class="normal">901</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="nd">@staticmethod</span>
<span class="k">def</span><span class="w"> </span><span class="nf">scaled_uniform</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tensor</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Creates a tensor with the given shape, filled with random values from a uniform distribution</span>
<span class="sd"> over the interval `[-prod(shape)**-0.5, prod(shape)**-0.5)`.</span>
<span class="sd"> You can pass in `dtype` and `device` keyword arguments to control the data type and device of the tensor.</span>
<span class="sd"> Additionally, all other keyword arguments are passed to the constructor of the tensor.</span>
<span class="sd"> ```python exec=&quot;true&quot; source=&quot;above&quot; session=&quot;tensor&quot; result=&quot;python&quot;</span>
<span class="sd"> Tensor.manual_seed(42)</span>
<span class="sd"> print(Tensor.scaled_uniform(2, 3).numpy())</span>
<span class="sd"> ```</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="n">Tensor</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="n">low</span><span class="o">=-</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">high</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span><span class="o">.</span><span class="n">mul</span><span class="p">(</span><span class="n">prod</span><span class="p">(</span><span class="n">argfix</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">))</span><span class="o">**-</span><span class="mf">0.5</span><span class="p">)</span>
</code></pre></div></td></tr></table></div>
</details>
</div>
</div>
<div class="doc doc-object doc-function">
<h3 id="tinygrad.Tensor.glorot_uniform" class="doc doc-heading">
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">glorot_uniform</span>
<span class="doc doc-labels">
<small class="doc doc-label doc-label-staticmethod"><code>staticmethod</code></small>
</span>
<a href="#tinygrad.Tensor.glorot_uniform" class="headerlink" title="Permanent link">¤</a></h3>
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">glorot_uniform</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n"><a class="autorefs autorefs-internal" title="&lt;code class=&quot;doc-symbol doc-symbol-heading doc-symbol-class&quot;&gt;&lt;/code&gt; &lt;span class=&quot;doc doc-object-name doc-class-name&quot;&gt;Tensor&lt;/span&gt; (&lt;code&gt;tinygrad.tensor.Tensor&lt;/code&gt;)" href="../#tinygrad.Tensor">Tensor</a></span>
</code></pre></div>
<div class="doc doc-contents first">
<p><a href="https://www.tensorflow.org/api_docs/python/tf/keras/initializers/GlorotUniform">https://www.tensorflow.org/api_docs/python/tf/keras/initializers/GlorotUniform</a></p>
<p>You can pass in <code class="language-python highlight"><span class="n">dtype</span></code> and <code class="language-python highlight"><span class="n">device</span></code> keyword arguments to control the data type and device of the tensor.
Additionally, all other keyword arguments are passed to the constructor of the tensor.</p>
<div class="language-python highlight"><pre><span></span><code><span class="n">Tensor</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">Tensor</span><span class="o">.</span><span class="n">glorot_uniform</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
</code></pre></div>
<div class="language-python highlight"><pre><span></span><code><span class="p">[[</span> <span class="mf">1.0889</span> <span class="mf">0.197</span> <span class="o">-</span><span class="mf">0.6079</span><span class="p">]</span>
<span class="p">[</span> <span class="mf">0.5588</span> <span class="mf">0.8887</span> <span class="mf">0.7994</span><span class="p">]]</span>
</code></pre></div>
<details class="mkdocstrings-source">
<summary>Source code in <code>tinygrad/tensor.py</code></summary>
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">904</span>
<span class="normal">905</span>
<span class="normal">906</span>
<span class="normal">907</span>
<span class="normal">908</span>
<span class="normal">909</span>
<span class="normal">910</span>
<span class="normal">911</span>
<span class="normal">912</span>
<span class="normal">913</span>
<span class="normal">914</span>
<span class="normal">915</span>
<span class="normal">916</span>
<span class="normal">917</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="nd">@staticmethod</span>
<span class="k">def</span><span class="w"> </span><span class="nf">glorot_uniform</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tensor</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> &lt;https://www.tensorflow.org/api_docs/python/tf/keras/initializers/GlorotUniform&gt;</span>
<span class="sd"> You can pass in `dtype` and `device` keyword arguments to control the data type and device of the tensor.</span>
<span class="sd"> Additionally, all other keyword arguments are passed to the constructor of the tensor.</span>
<span class="sd"> ```python exec=&quot;true&quot; source=&quot;above&quot; session=&quot;tensor&quot; result=&quot;python&quot;</span>
<span class="sd"> Tensor.manual_seed(42)</span>
<span class="sd"> print(Tensor.glorot_uniform(2, 3).numpy())</span>
<span class="sd"> ```</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="n">Tensor</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="n">low</span><span class="o">=-</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">high</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span><span class="o">.</span><span class="n">mul</span><span class="p">((</span><span class="mi">6</span><span class="o">/</span><span class="p">(</span><span class="n">argfix</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span><span class="o">+</span><span class="n">prod</span><span class="p">(</span><span class="n">argfix</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">)[</span><span class="mi">1</span><span class="p">:])))</span><span class="o">**</span><span class="mf">0.5</span><span class="p">)</span>
</code></pre></div></td></tr></table></div>
</details>
</div>
</div>
<div class="doc doc-object doc-function">
<h3 id="tinygrad.Tensor.kaiming_uniform" class="doc doc-heading">
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">kaiming_uniform</span>
<span class="doc doc-labels">
<small class="doc doc-label doc-label-staticmethod"><code>staticmethod</code></small>
</span>
<a href="#tinygrad.Tensor.kaiming_uniform" class="headerlink" title="Permanent link">¤</a></h3>
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">kaiming_uniform</span><span class="p">(</span>
<span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="n">a</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-internal" title="&lt;code class=&quot;doc-symbol doc-symbol-heading doc-symbol-method&quot;&gt;&lt;/code&gt; &lt;span class=&quot;doc doc-object-name doc-function-name&quot;&gt;float&lt;/span&gt; (&lt;code&gt;tinygrad.tensor.Tensor.float&lt;/code&gt;)" href="../elementwise/#tinygrad.Tensor.float">float</a></span> <span class="o">=</span> <span class="mf">0.01</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n"><a class="autorefs autorefs-internal" title="&lt;code class=&quot;doc-symbol doc-symbol-heading doc-symbol-class&quot;&gt;&lt;/code&gt; &lt;span class=&quot;doc doc-object-name doc-class-name&quot;&gt;Tensor&lt;/span&gt; (&lt;code&gt;tinygrad.tensor.Tensor&lt;/code&gt;)" href="../#tinygrad.Tensor">Tensor</a></span>
</code></pre></div>
<div class="doc doc-contents first">
<p><a href="https://pytorch.org/docs/stable/_modules/torch/nn/init.html#kaiming_uniform_">https://pytorch.org/docs/stable/_modules/torch/nn/init.html#kaiming_uniform_</a></p>
<p>You can pass in <code class="language-python highlight"><span class="n">dtype</span></code> and <code class="language-python highlight"><span class="n">device</span></code> keyword arguments to control the data type and device of the tensor.
Additionally, all other keyword arguments are passed to the constructor of the tensor.</p>
<div class="language-python highlight"><pre><span></span><code><span class="n">Tensor</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">Tensor</span><span class="o">.</span><span class="n">kaiming_uniform</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
</code></pre></div>
<div class="language-python highlight"><pre><span></span><code><span class="p">[[</span> <span class="mf">1.4058</span> <span class="mf">0.2543</span> <span class="o">-</span><span class="mf">0.7847</span><span class="p">]</span>
<span class="p">[</span> <span class="mf">0.7214</span> <span class="mf">1.1473</span> <span class="mf">1.032</span> <span class="p">]]</span>
</code></pre></div>
<details class="mkdocstrings-source">
<summary>Source code in <code>tinygrad/tensor.py</code></summary>
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">920</span>
<span class="normal">921</span>
<span class="normal">922</span>
<span class="normal">923</span>
<span class="normal">924</span>
<span class="normal">925</span>
<span class="normal">926</span>
<span class="normal">927</span>
<span class="normal">928</span>
<span class="normal">929</span>
<span class="normal">930</span>
<span class="normal">931</span>
<span class="normal">932</span>
<span class="normal">933</span>
<span class="normal">934</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="nd">@staticmethod</span>
<span class="k">def</span><span class="w"> </span><span class="nf">kaiming_uniform</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="n">a</span><span class="p">:</span><span class="nb">float</span> <span class="o">=</span> <span class="mf">0.01</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tensor</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> &lt;https://pytorch.org/docs/stable/_modules/torch/nn/init.html#kaiming_uniform_&gt;</span>
<span class="sd"> You can pass in `dtype` and `device` keyword arguments to control the data type and device of the tensor.</span>
<span class="sd"> Additionally, all other keyword arguments are passed to the constructor of the tensor.</span>
<span class="sd"> ```python exec=&quot;true&quot; source=&quot;above&quot; session=&quot;tensor&quot; result=&quot;python&quot;</span>
<span class="sd"> Tensor.manual_seed(42)</span>
<span class="sd"> print(Tensor.kaiming_uniform(2, 3).numpy())</span>
<span class="sd"> ```</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">bound</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mf">3.0</span><span class="p">)</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mf">2.0</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">a</span> <span class="o">**</span> <span class="mi">2</span><span class="p">))</span> <span class="o">/</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">prod</span><span class="p">(</span><span class="n">argfix</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">)[</span><span class="mi">1</span><span class="p">:]))</span>
<span class="k">return</span> <span class="n">Tensor</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="n">low</span><span class="o">=-</span><span class="n">bound</span><span class="p">,</span> <span class="n">high</span><span class="o">=</span><span class="n">bound</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
</code></pre></div></td></tr></table></div>
</details>
</div>
</div>
<div class="doc doc-object doc-function">
<h3 id="tinygrad.Tensor.kaiming_normal" class="doc doc-heading">
<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code> <span class="doc doc-object-name doc-function-name">kaiming_normal</span>
<span class="doc doc-labels">
<small class="doc doc-label doc-label-staticmethod"><code>staticmethod</code></small>
</span>
<a href="#tinygrad.Tensor.kaiming_normal" class="headerlink" title="Permanent link">¤</a></h3>
<div class="language-python doc-signature highlight"><pre><span></span><code><span class="nf">kaiming_normal</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="n">a</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-internal" title="&lt;code class=&quot;doc-symbol doc-symbol-heading doc-symbol-method&quot;&gt;&lt;/code&gt; &lt;span class=&quot;doc doc-object-name doc-function-name&quot;&gt;float&lt;/span&gt; (&lt;code&gt;tinygrad.tensor.Tensor.float&lt;/code&gt;)" href="../elementwise/#tinygrad.Tensor.float">float</a></span> <span class="o">=</span> <span class="mf">0.01</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n"><a class="autorefs autorefs-internal" title="&lt;code class=&quot;doc-symbol doc-symbol-heading doc-symbol-class&quot;&gt;&lt;/code&gt; &lt;span class=&quot;doc doc-object-name doc-class-name&quot;&gt;Tensor&lt;/span&gt; (&lt;code&gt;tinygrad.tensor.Tensor&lt;/code&gt;)" href="../#tinygrad.Tensor">Tensor</a></span>
</code></pre></div>
<div class="doc doc-contents first">
<p><a href="https://pytorch.org/docs/stable/_modules/torch/nn/init.html#kaiming_normal_">https://pytorch.org/docs/stable/_modules/torch/nn/init.html#kaiming_normal_</a></p>
<p>You can pass in <code class="language-python highlight"><span class="n">dtype</span></code> and <code class="language-python highlight"><span class="n">device</span></code> keyword arguments to control the data type and device of the tensor.
Additionally, all other keyword arguments are passed to the constructor of the tensor.</p>
<div class="language-python highlight"><pre><span></span><code><span class="n">Tensor</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">Tensor</span><span class="o">.</span><span class="n">kaiming_normal</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
</code></pre></div>
<div class="language-python highlight"><pre><span></span><code><span class="p">[[</span> <span class="mf">0.7984</span> <span class="mf">0.3819</span> <span class="mf">0.4512</span><span class="p">]</span>
<span class="p">[</span><span class="o">-</span><span class="mf">0.2685</span> <span class="o">-</span><span class="mf">0.6985</span> <span class="mf">0.2247</span><span class="p">]]</span>
</code></pre></div>
<details class="mkdocstrings-source">
<summary>Source code in <code>tinygrad/tensor.py</code></summary>
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">937</span>
<span class="normal">938</span>
<span class="normal">939</span>
<span class="normal">940</span>
<span class="normal">941</span>
<span class="normal">942</span>
<span class="normal">943</span>
<span class="normal">944</span>
<span class="normal">945</span>
<span class="normal">946</span>
<span class="normal">947</span>
<span class="normal">948</span>
<span class="normal">949</span>
<span class="normal">950</span>
<span class="normal">951</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="nd">@staticmethod</span>
<span class="k">def</span><span class="w"> </span><span class="nf">kaiming_normal</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="n">a</span><span class="p">:</span><span class="nb">float</span> <span class="o">=</span> <span class="mf">0.01</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tensor</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> &lt;https://pytorch.org/docs/stable/_modules/torch/nn/init.html#kaiming_normal_&gt;</span>
<span class="sd"> You can pass in `dtype` and `device` keyword arguments to control the data type and device of the tensor.</span>
<span class="sd"> Additionally, all other keyword arguments are passed to the constructor of the tensor.</span>
<span class="sd"> ```python exec=&quot;true&quot; source=&quot;above&quot; session=&quot;tensor&quot; result=&quot;python&quot;</span>
<span class="sd"> Tensor.manual_seed(42)</span>
<span class="sd"> print(Tensor.kaiming_normal(2, 3).numpy())</span>
<span class="sd"> ```</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">std</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mf">2.0</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">a</span> <span class="o">**</span> <span class="mi">2</span><span class="p">))</span> <span class="o">/</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">prod</span><span class="p">(</span><span class="n">argfix</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">)[</span><span class="mi">1</span><span class="p">:]))</span>
<span class="k">return</span> <span class="n">Tensor</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="n">mean</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">std</span><span class="o">=</span><span class="n">std</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
</code></pre></div></td></tr></table></div>
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