python.assert
=================
dynamic_shape_assert
^^^^^^^^^^^^^^^^^^^^

.. note::

    Tags: :doc:`python.assert <python.assert>`

    Support Level: SUPPORTED

Original source code:

.. code-block:: python

    import torch
    
    
    
    def dynamic_shape_assert(x):
        """
        A basic usage of python assertion.
        """
        # assertion with error message
        assert x.shape[0] > 2, f"{x.shape[0]} is greater than 2"
        # assertion without error message
        assert x.shape[0] > 1
        return x
    

Result:

.. code-block::

    ExportedProgram:
        class GraphModule(torch.nn.Module):
            def forward(self, l_x_: "f32[3, 2]"):
                return (l_x_,)
                
    Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='l_x_'), target=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='l_x_'), target=None)])
    Range constraints: {}
    Equality constraints: []
    


list_contains
^^^^^^^^^^^^^

.. note::

    Tags: :doc:`torch.dynamic-shape <torch.dynamic-shape>`, :doc:`python.data-structure <python.data-structure>`, :doc:`python.assert <python.assert>`

    Support Level: SUPPORTED

Original source code:

.. code-block:: python

    import torch
    
    
    
    def list_contains(x):
        """
        List containment relation can be checked on a dynamic shape or constants.
        """
        assert x.size(-1) in [6, 2]
        assert x.size(0) not in [4, 5, 6]
        assert "monkey" not in ["cow", "pig"]
        return x + x
    

Result:

.. code-block::

    ExportedProgram:
        class GraphModule(torch.nn.Module):
            def forward(self, l_x_: "f32[3, 2]"):
                    add: "f32[3, 2]" = torch.ops.aten.add.Tensor(l_x_, l_x_);  l_x_ = None
                return (add,)
                
    Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='l_x_'), target=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='add'), target=None)])
    Range constraints: {}
    Equality constraints: []