torchoutil.types.guards module

is_bool_tensor(
x: Any,
) → typing_extensions.TypeIs[BoolTensor][source]
is_bool_tensor1d(
x: Any,
) → typing_extensions.TypeIs[BoolTensor1D][source]
is_complex_tensor(
x: Any,
) → typing_extensions.TypeIs[ComplexFloatingTensor][source]
is_dict_str_tensor(
x: Any,
) → typing_extensions.TypeIs[Dict[str, Tensor]][source]
is_floating_tensor(
x: Any,
) → typing_extensions.TypeIs[FloatingTensor][source]
is_integral_dtype(
dtype: dtype,
) → bool[source]
is_integral_tensor(
x: Any,
) → typing_extensions.TypeIs[IntegralTensor][source]
is_integral_tensor1d(
x: Any,
) → typing_extensions.TypeIs[IntegralTensor1D][source]
is_iterable_tensor(
x: Any,
) → typing_extensions.TypeIs[Iterable[Tensor]][source]
is_list_tensor(
x: Any,
) → typing_extensions.TypeIs[List[Tensor]][source]
is_number_like(
x: Any,
) → typing_extensions.TypeGuard[bool | int | float | complex | ndarray | number | Tensor0D][source]

Returns True if input is a scalar number.

Accepted numbers-like objects are: - Python numbers (int, float, bool, complex) - Numpy zero-dimensional arrays - Numpy numbers - PyTorch zero-dimensional tensors

is_scalar_like(
x: Any,
) → typing_extensions.TypeGuard[bool | int | float | complex | None | str | bytes | ndarray | generic | Tensor0D][source]

Returns True if input is a scalar number.

Accepted scalar-like objects are: - Python scalars like (int, float, bool, complex, None, str, bytes) - Numpy zero-dimensional arrays - Numpy generic - PyTorch zero-dimensional tensors

is_tensor0d(
x: Any,
) → typing_extensions.TypeIs[Tensor0D][source]
is_tensor_like(
x: Any,
) → typing_extensions.TypeIs[Tensor | ndarray][source]
is_tensor_or_array(
x: Any,
) → typing_extensions.TypeIs[Tensor | ndarray][source]
is_tuple_tensor(
x: Any,
) → typing_extensions.TypeIs[Tuple[Tensor, ...]][source]