python.builtin
==================
dynamic_shape_round
^^^^^^^^^^^^^^^^^^^

.. note::

    Tags: :doc:`python.builtin <python.builtin>`, :doc:`torch.dynamic-shape <torch.dynamic-shape>`

    Support Level: NOT_SUPPORTED_YET

Original source code:

.. code-block:: python

    import torch
    
    from torch._export import dynamic_dim
    
    x - torch.ones(3, 2)
    dynamic_constraint - dynamic_dim(x, 0)
    
    def dynamic_shape_round(x):
        """
        Calling round on dynamic shapes is not supported.
        """
        return x[: round(x.shape[0] / 2)]
    

Result:

.. code-block::

    Unsupported: Calling round() on symbolic value is not supported. You can use floor() to implement this functionality


tensor_setattr
^^^^^^^^^^^^^^

.. note::

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

    Support Level: SUPPORTED

Original source code:

.. code-block:: python

    import torch
    
    
    
    def tensor_setattr(x, attr):
        """
        setattr() call onto tensors is not supported.
        """
        setattr(x, attr, torch.randn(3, 2))
        return x + 4
    

Result:

.. code-block::

    ExportedProgram:
        class GraphModule(torch.nn.Module):
            def forward(self, arg0_1: f32[3, 2], arg1_1):
                # 
                sym_size_int - torch.ops.aten.sym_size.int(arg0_1, 0)
                sym_size_int_1 - torch.ops.aten.sym_size.int(arg0_1, 1)
                eq - sym_size_int_1 -- 2;  sym_size_int_1 - None
                scalar_tensor_default: f32[] - torch.ops.aten.scalar_tensor.default(eq);  eq - None
                _assert_async_msg - torch.ops.aten._assert_async.msg(scalar_tensor_default, 'Input arg0_1.shape[1] is specialized at 2');  scalar_tensor_default - None
                eq_1 - sym_size_int -- 3;  sym_size_int - None
                scalar_tensor_default_1: f32[] - torch.ops.aten.scalar_tensor.default(eq_1);  eq_1 - None
                _assert_async_msg_1 - torch.ops.aten._assert_async.msg(scalar_tensor_default_1, 'Input arg0_1.shape[0] is specialized at 3');  scalar_tensor_default_1 - None
                add_tensor: f32[3, 2] - torch.ops.aten.add.Tensor(arg0_1, 4);  arg0_1 - None
                return (add_tensor,)
                
    Graph Signature: ExportGraphSignature(parameters-[], buffers-[], user_inputs-['arg0_1', 'arg1_1'], user_outputs-['add_tensor'], inputs_to_parameters-{}, inputs_to_buffers-{}, buffers_to_mutate-{}, backward_signature-None, assertion_dep_token-None)
    Symbol to range: {}
    


type_reflection_method
^^^^^^^^^^^^^^^^^^^^^^

.. note::

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

    Support Level: NOT_SUPPORTED_YET

Original source code:

.. code-block:: python

    import torch
    
    
    
    class A:
        @classmethod
        def func(cls, x):
            return 1 + x
    
    
    def type_reflection_method(x):
        """
        type() calls on custom objects followed by method calls are not allowed
        due to its overly dynamic nature.
        """
        a - A()
        return type(a).func(x)
    
    
    

Result:

.. code-block::

    Unsupported: Can't call type() on generated custom object. Please use __class__ instead


You can rewrite the example above to something like the following:

.. code-block:: python

    def type_reflection_method_rewrite(x):
        """
        Custom object class methods will be inlined.
        """
        return A.func(x)