diff --git a/test/test_rangeify.py b/test/test_rangeify.py
index a7cb8a4a54..d15b993965 100644
--- a/test/test_rangeify.py
+++ b/test/test_rangeify.py
@@ -38,6 +38,13 @@ class TestRangeifyOpt(unittest.TestCase):
     Xsel, Ysel = X[sel], Y[sel]
     Tensor.realize(Xsel, Ysel)
 
+  def test_resnetconv(self):
+    conv1 = nn.Conv2d(3, 8, kernel_size=7, stride=2, bias=False, padding=3)
+    conv1.weight.replace(conv1.weight.empty_like())
+    x = Tensor.empty(1, 3, 56, 56)
+    x = conv1(x).pad([1,1,1,1])+1
+    x.realize()
+
 @unittest.skipIf(RANGEIFY<1, "tests only for RANGEIFY")
 class TestRangeify(unittest.TestCase):
   def test_groupnorm(self):
diff --git a/tinygrad/tensor.py b/tinygrad/tensor.py
index 81ef5013e7..76969ea11d 100644
--- a/tinygrad/tensor.py
+++ b/tinygrad/tensor.py
@@ -456,6 +456,13 @@ class Tensor(MathTrait):
     device = tuple(Device.canonicalize(d) for d in device) if isinstance(device, tuple) else Device.canonicalize(device)
     return Tensor(UOp.new_buffer(device, size, dtype), device, dtype, **kwargs).shrink(((0,prod(shape)),)).reshape(shape)
 
+  def empty_like(self, **kwargs) -> Tensor:
+    """
+    Creates an empty tensor with the same shape as `self`.
+    If `dtype` is not specified, the dtype of `self` is used.
+    """
+    return Tensor.empty(self.shape, dtype=kwargs.pop("dtype", self.dtype), device=kwargs.pop("device", self.device), **kwargs)
+
   @staticmethod
   def from_blob(ptr:int, shape:tuple[int, ...], **kwargs) -> Tensor:
     """