python.data-structure
=========================
dictionary
^^^^^^^^^^

.. note::

    Tags: :doc:`python.data-structure <python.data-structure>`

    Support Level: SUPPORTED

Original source code:

.. code-block:: python

    import torch
    
    
    
    def dictionary(x, y):
        """
        Dictionary structures are inlined and flattened along tracing.
        """
        elements = {}
        elements["x2"] = x * x
        y = y * elements["x2"]
        return {"y": y}
    

Result:

.. code-block::

    ExportedProgram:
        class GraphModule(torch.nn.Module):
            def forward(self, l_x_: "f32[3, 2]", l_y_: "i64[]"):
                    mul: "f32[3, 2]" = torch.ops.aten.mul.Tensor(l_x_, l_x_);  l_x_ = None
                
                    mul_1: "f32[3, 2]" = torch.ops.aten.mul.Tensor(l_y_, mul);  l_y_ = mul = None
                return (mul_1,)
                
    Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='l_x_'), target=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='l_y_'), target=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='mul_1'), target=None)])
    Range constraints: {}
    Equality constraints: []
    


fn_with_kwargs
^^^^^^^^^^^^^^

.. note::

    Tags: :doc:`python.data-structure <python.data-structure>`

    Support Level: SUPPORTED

Original source code:

.. code-block:: python

    import torch
    
    
    
        ),
        tags={"python.data-structure"},
        support_level=SupportLevel.SUPPORTED,
    )
    def fn_with_kwargs(pos0, tuple0, *myargs, mykw0, **mykwargs):
        """
        Keyword arguments are not supported at the moment.
        """
        out = pos0
        for arg in tuple0:
            out = out * arg
        for arg in myargs:
            out = out * arg
        out = out * mykw0
        out = out * mykwargs["input0"] * mykwargs["input1"]
        return out
    

Result:

.. code-block::

    ExportedProgram:
        class GraphModule(torch.nn.Module):
            def forward(self, out: "f32[4]", arg: "f32[4]", arg_1: "f32[4]", arg_2: "f32[4]", arg_3: "f32[4]", l_mykw0_: "f32[4]", l_mykwargs_input0_: "f32[4]", l_mykwargs_input1_: "f32[4]"):
                    mul: "f32[4]" = torch.ops.aten.mul.Tensor(out, arg);  out = arg = None
                mul_1: "f32[4]" = torch.ops.aten.mul.Tensor(mul, arg_1);  mul = arg_1 = None
                
                    mul_2: "f32[4]" = torch.ops.aten.mul.Tensor(mul_1, arg_2);  mul_1 = arg_2 = None
                mul_3: "f32[4]" = torch.ops.aten.mul.Tensor(mul_2, arg_3);  mul_2 = arg_3 = None
                
                    mul_4: "f32[4]" = torch.ops.aten.mul.Tensor(mul_3, l_mykw0_);  mul_3 = l_mykw0_ = None
                
                    mul_5: "f32[4]" = torch.ops.aten.mul.Tensor(mul_4, l_mykwargs_input0_);  mul_4 = l_mykwargs_input0_ = None
                mul_6: "f32[4]" = torch.ops.aten.mul.Tensor(mul_5, l_mykwargs_input1_);  mul_5 = l_mykwargs_input1_ = None
                return (mul_6,)
                
    Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='out'), target=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='arg'), target=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='arg_1'), target=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='arg_2'), target=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='arg_3'), target=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='l_mykw0_'), target=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='l_mykwargs_input0_'), target=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='l_mykwargs_input1_'), target=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='mul_6'), 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: []
    


list_unpack
^^^^^^^^^^^

.. note::

    Tags: :doc:`python.data-structure <python.data-structure>`, :doc:`python.control-flow <python.control-flow>`

    Support Level: SUPPORTED

Original source code:

.. code-block:: python

    from typing import List
    
    import torch
    
    
    
    def list_unpack(args: List[torch.Tensor]):
        """
        Lists are treated as static construct, therefore unpacking should be
        erased after tracing.
        """
        x, *y = args
        return x + y[0]
    

Result:

.. code-block::

    ExportedProgram:
        class GraphModule(torch.nn.Module):
            def forward(self, x: "f32[3, 2]", l_args_1_: "i64[]", arg2: "i64[]"):
                    add: "f32[3, 2]" = torch.ops.aten.add.Tensor(x, l_args_1_);  x = l_args_1_ = None
                return (add,)
                
    Graph signature: ExportGraphSignature(input_specs=[InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='x'), target=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='l_args_1_'), target=None), InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='arg2'), target=None)], output_specs=[OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='add'), target=None)])
    Range constraints: {}
    Equality constraints: []