Function isinstance

torch/jit/__init__.py:196–236

Provide container type refinement in TorchScript. It can refine parameterized containers of the List, Dict, Tuple, and Optional types. E.g. ``List[str]``, ``Dict[str, List[torch.Tensor]]``, ``Optional[Tuple[int,str,int]]``. It can also refine basic types such as bools and ints that

(obj, target_type)

# for torch.jit.isinstance
def isinstance(obj, target_type):
    """
    Provide container type refinement in TorchScript.

    It can refine parameterized containers of the List, Dict, Tuple, and Optional types. E.g. ``List[str]``,
    ``Dict[str, List[torch.Tensor]]``, ``Optional[Tuple[int,str,int]]``. It can also
    refine basic types such as bools and ints that are available in TorchScript.

    Args:
        obj: object to refine the type of
        target_type: type to try to refine obj to
    Returns:
        ``bool``: True if obj was successfully refined to the type of target_type,
            False otherwise with no new type refinement


    Example (using ``torch.jit.isinstance`` for type refinement):
    .. testcode::

        import torch
        from typing import Any, Dict, List

        class MyModule(torch.nn.Module):
            def __init__(self):
                super().__init__()

            def forward(self, input: Any): # note the Any type
                if torch.jit.isinstance(input, List[torch.Tensor]):
                    for t in input:
                        y = t.clamp(0, 0.5)
                elif torch.jit.isinstance(input, Dict[str, str]):
                    for val in input.values():
                        print(val)

        m = torch.jit.script(MyModule())
        x = [torch.rand(3,3), torch.rand(4,3)]
        m(x)
        y = {"key1":"val1","key2":"val2"}
        m(y)
    """
    return _isinstance(obj, target_type)


class strict_fusion: