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266 lines
8.3 KiB
Markdown
266 lines
8.3 KiB
Markdown
# Debug
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In this section, you will learn how to debug the compilation process easily as well as how to get help in case you cannot resolve your issue.
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## Debug Artifacts
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**Concrete-Numpy** has an artifact system to simplify the process of debugging issues.
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### Automatic export.
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In case of compilation failures, artifacts are exported automatically to the `.artifacts` directory under the working directory. Let's intentionally create a compilation failure to show what kinds of things are exported.
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```python
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def f(x):
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return np.sin(x)
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```
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This function fails to compile because **Concrete-Numpy** does not support floating-point outputs. When you try to compile it, an exception will be raised and the artifacts will be exported automatically. If you go the `.artifacts` directory under the working directory, you'll see the following files:
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#### environment.txt
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This file contains information about your setup (i.e., your operating system and python version).
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```
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Linux-5.12.13-arch1-2-x86_64-with-glibc2.29 #1 SMP PREEMPT Fri, 25 Jun 2021 22:56:51 +0000
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Python 3.8.10
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```
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#### requirements.txt
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This file contains information about python packages and their versions installed on your system.
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```
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alabaster==0.7.12
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appdirs==1.4.4
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argon2-cffi==21.1.0
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...
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wheel==0.37.0
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widgetsnbextension==3.5.1
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wrapt==1.12.1
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```
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#### function.txt
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This file contains information about the function you tried to compile.
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```
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def f(x):
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return np.sin(x)
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```
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#### parameters.txt
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This file contains information about the encryption status of the parameters of the function you tried to compile.
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```
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x :: encrypted
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```
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#### 1.initial.graph.txt
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This file contains the textual representation of the initial computation graph right after tracing.
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```
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%0 = x # EncryptedScalar<uint3>
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%1 = sin(%0) # EncryptedScalar<float64>
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return %1
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```
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#### 1.initial.graph.png
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This file contains the visual representation of the initial computation graph right after tracing.
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#### 2.final.graph.txt
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This file contains the textual representation of the final computation graph right before MLIR conversion.
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```
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%0 = x # EncryptedScalar<uint3>
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%1 = sin(%0) # EncryptedScalar<float64>
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return %1
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```
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#### 2.final.graph.png
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This file contains the visual representation of the final computation graph right before MLIR conversion.
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#### traceback.txt
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This file contains information about the error you received.
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```
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Traceback (most recent call last):
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File "/home/default/Documents/Projects/Zama/hdk/concrete/numpy/compilation/compiler.py", line 320, in compile
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mlir = GraphConverter.convert(self.graph, virtual=self.configuration.virtual)
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File "/home/default/Documents/Projects/Zama/hdk/concrete/numpy/mlir/graph_converter.py", line 298, in convert
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GraphConverter._check_graph_convertibility(graph)
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File "/home/default/Documents/Projects/Zama/hdk/concrete/numpy/mlir/graph_converter.py", line 175, in _check_graph_convertibility
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raise RuntimeError(message)
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RuntimeError: Function you are trying to compile cannot be converted to MLIR
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%0 = x # EncryptedScalar<uint4>
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%1 = sin(%0) # EncryptedScalar<float64>
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ only integer operations are supported
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return %1
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```
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### Manual export.
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Manual exports are mostly used for visualization. Nonetheless, they can be very useful for demonstrations. Here is how to perform one:
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```python
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import concrete.numpy as cnp
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import numpy as np
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artifacts = cnp.DebugArtifacts("/tmp/custom/export/path")
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@cnp.compiler({"x": "encrypted"})
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def f(x):
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return 127 - (50 * (np.sin(x) + 1)).astype(np.int64)
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inputset = range(2 ** 3)
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circuit = f.compile(inputset, artifacts=artifacts)
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artifacts.export()
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```
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If you go to the `/tmp/custom/export/path` directory, you'll see the following files:
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#### 1.initial.graph.txt
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This file contains the textual representation of the initial computation graph right after tracing.
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```
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%0 = 127 # ClearScalar<uint7>
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%1 = 50 # ClearScalar<uint6>
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%2 = 1 # ClearScalar<uint1>
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%3 = x # EncryptedScalar<uint1>
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%4 = sin(%3) # EncryptedScalar<float64>
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%5 = add(%4, %2) # EncryptedScalar<float64>
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%6 = multiply(%1, %5) # EncryptedScalar<float64>
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%7 = astype(%6, dtype=int_) # EncryptedScalar<uint1>
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%8 = subtract(%0, %7) # EncryptedScalar<uint1>
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return %8
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```
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#### 1.initial.graph.png
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This file contains the visual representation of the initial computation graph right after tracing.
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#### 2.after-float-fuse-0.graph.txt
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This file contains the textual representation of the intermediate computation graph after fusing.
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```
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%0 = 127 # ClearScalar<uint7>
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%1 = x # EncryptedScalar<uint1>
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%2 = subgraph(%1) # EncryptedScalar<uint1>
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%3 = subtract(%0, %2) # EncryptedScalar<uint1>
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return %3
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Subgraphs:
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%2 = subgraph(%1):
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%0 = 50 # ClearScalar<uint6>
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%1 = 1 # ClearScalar<uint1>
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%2 = input # EncryptedScalar<uint1>
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%3 = sin(%2) # EncryptedScalar<float64>
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%4 = add(%3, %1) # EncryptedScalar<float64>
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%5 = multiply(%0, %4) # EncryptedScalar<float64>
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%6 = astype(%5, dtype=int_) # EncryptedScalar<uint1>
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return %6
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```
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#### 2.after-fusing.graph.png
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This file contains the visual representation of the intermediate computation graph after fusing.
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#### 3.final.graph.txt
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This file contains the textual representation of the final computation graph right before MLIR conversion.
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```
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%0 = 127 # ClearScalar<uint7>
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%1 = x # EncryptedScalar<uint3>
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%2 = subgraph(%1) # EncryptedScalar<uint7>
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%3 = subtract(%0, %2) # EncryptedScalar<uint7>
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return %3
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Subgraphs:
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%2 = subgraph(%1):
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%0 = 50 # ClearScalar<uint6>
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%1 = 1 # ClearScalar<uint1>
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%2 = input # EncryptedScalar<uint1>
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%3 = sin(%2) # EncryptedScalar<float64>
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%4 = add(%3, %1) # EncryptedScalar<float64>
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%5 = multiply(%0, %4) # EncryptedScalar<float64>
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%6 = astype(%5, dtype=int_) # EncryptedScalar<uint1>
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return %6
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```
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#### 3.final.graph.png
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This file contains the visual representation of the final computation graph right before MLIR conversion.
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#### bounds.txt
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This file contains information about the bounds of the final computation graph of the function you are compiling using the inputset you provide.
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```
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%0 :: [127, 127]
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%1 :: [0, 7]
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%2 :: [2, 95]
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%3 :: [32, 125]
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```
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#### mlir.txt
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This file contains information about the MLIR of the function you compiled using the inputset you provided.
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```
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module {
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func @main(%arg0: !FHE.eint<7>) -> !FHE.eint<7> {
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%c127_i8 = arith.constant 127 : i8
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%cst = arith.constant dense<"..."> : tensor<128xi64>
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%0 = "FHE.apply_lookup_table"(%arg0, %cst) : (!FHE.eint<7>, tensor<128xi64>) -> !FHE.eint<7>
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%1 = "FHE.sub_int_eint"(%c127_i8, %0) : (i8, !FHE.eint<7>) -> !FHE.eint<7>
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return %1 : !FHE.eint<7>
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}
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}
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```
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## Asking the community
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You can seek help with your issue by asking a question directly in the [community forum](https://community.zama.ai/).
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## Submitting an issue
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If you cannot find a solution in the community forum, or you found a bug in the library, you could create an issue in our GitHub repository.
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In case of a bug:
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* try to minimize randomness
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* try to minimize your function as much as possible while keeping the bug - this will help to fix the bug faster
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* try to include your inputset in the issue
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* try to include reproduction steps in the issue
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* try to include debug artifacts in the issue
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In case of a feature request:
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* try to give a minimal example of the desired behavior
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* try to explain your use case
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