torchwrench.nn.functional.predicate module¶

torchwrench.nn.functional.predicate.all_eq(x: Tensor | ndarray | bool | int | float | complex | None | str | bytes | generic | Tensor0D | Iterable, dim: None = None) → bool[source]¶
torchwrench.nn.functional.predicate.all_eq(x: T_TensorOrArray, dim: int) → T_TensorOrArray: Check if all elements are equal in a tensor, ndarray, iterable or scalar object.

torchwrench.nn.functional.predicate.all_ne(x: Tensor | ndarray | bool | int | float | complex | None | str | bytes | generic | Tensor0D | Iterable) → bool[source]¶: Check if all elements are NOT equal in a tensor, ndarray, iterable or scalar object.

torchwrench.nn.functional.predicate.is_complex(x: Tensor) → TypeGuard[ComplexFloatingTensor][source]¶
torchwrench.nn.functional.predicate.is_complex(x: ndarray) → TypeGuard[ndarray]
torchwrench.nn.functional.predicate.is_complex(x: complex) → TypeGuard[complex]
torchwrench.nn.functional.predicate.is_complex(x: Any) → TypeGuard[ComplexFloatingTensor | ndarray | complex]: Returns True if object is a/contains complex-valued object(s).

torchwrench.nn.functional.predicate.is_convertible_to_tensor(x: Any) → bool[source]¶

Returns True if inputs can be passed to torch.as_tensor function.

This function returns False for heterogeneous inputs like [[], 1], but this kind of value can be accepted by torch.as_tensor.

torchwrench.nn.functional.predicate.is_floating_point(x: Tensor) → TypeGuard[FloatingTensor][source]¶
torchwrench.nn.functional.predicate.is_floating_point(x: ndarray) → TypeGuard[ndarray]
torchwrench.nn.functional.predicate.is_floating_point(x: float) → TypeGuard[float]
torchwrench.nn.functional.predicate.is_floating_point(x: Any) → TypeGuard[FloatingTensor | ndarray | float]: Returns True if object is a/contains floating-point object(s).

torchwrench.nn.functional.predicate.is_full(x: Tensor | ndarray, target: Any = Ellipsis) → bool[source]¶: Check if all element are equal to target in a tensor or array. Accept an optional value ‘target’ to specified the expected value.

torchwrench.nn.functional.predicate.is_sorted(x: Tensor | ndarray | Iterable, *, reverse: bool = False, strict: bool = False) → bool[source]¶: Returns True if the sequence is sorted.

torchwrench.nn.functional.predicate.is_stackable(tensors: list[Any] | tuple[Any, ...]) → TypeGuard[list[Tensor] | tuple[Tensor, ...]][source]¶: Returns True if inputs can be passed to torch.stack function, i.e. contains a non-empty list or tuple of tensors with the same shape.

torchwrench.nn.functional.predicate.is_unique(x: Tensor | ndarray | bool | int | float | complex | None | str | bytes | generic | Tensor0D | Iterable) → bool[source]¶: Check if all elements are NOT equal in a tensor, ndarray, iterable or scalar object.