torchwrench.nn.functional.new module

torchwrench.nn.functional.new.arange(end: int | float | bool, *, out: Tensor | None = None, dtype: None = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None, requires_grad: bool = False, pin_memory: bool = False) → LongTensor1D[source]
torchwrench.nn.functional.new.arange(start: int | float | bool, end: int | float | bool, *, out: Tensor | None = None, dtype: None = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None, requires_grad: bool = False, pin_memory: bool = False) → LongTensor1D
torchwrench.nn.functional.new.arange(start: int | float | bool, end: int | float | bool, step: int | float | bool, *, out: Tensor | None = None, dtype: None = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None, requires_grad: bool = False, pin_memory: bool = False) → LongTensor1D
torchwrench.nn.functional.new.arange(end: int | float | bool, *, out: Tensor | None = None, dtype: dtype | str | DTypeEnum, device: device | None | 'default' | 'cuda_if_available' | str | int = None, requires_grad: bool = False, pin_memory: bool = False) → Tensor1D
torchwrench.nn.functional.new.arange(start: int | float | bool, end: int | float | bool, *, out: Tensor | None = None, dtype: dtype | str | DTypeEnum, device: device | None | 'default' | 'cuda_if_available' | str | int = None, requires_grad: bool = False, pin_memory: bool = False) → Tensor1D
torchwrench.nn.functional.new.arange(start: int | float | bool, end: int | float | bool, step: int | float | bool, *, out: Tensor | None = None, dtype: dtype | str | DTypeEnum, device: device | None | 'default' | 'cuda_if_available' | str | int = None, requires_grad: bool = False, pin_memory: bool = False) → Tensor1D
torchwrench.nn.functional.new.empty(size: Sequence[Never], /, *, dtype: dtype | None | 'default' | str | DTypeEnum = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None, memory_format: memory_format | None = None, out: Tensor | None = None, layout: layout | None = None, pin_memory: bool | None = False, requires_grad: bool | None = False) → Tensor0D[source]
torchwrench.nn.functional.new.empty(size: tuple[int], /, *, dtype: dtype | None | 'default' | str | DTypeEnum = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None, memory_format: memory_format | None = None, out: Tensor | None = None, layout: layout | None = None, pin_memory: bool | None = False, requires_grad: bool | None = False) → Tensor1D
torchwrench.nn.functional.new.empty(size: tuple[int, int], /, *, dtype: dtype | None | 'default' | str | DTypeEnum = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None, memory_format: memory_format | None = None, out: Tensor | None = None, layout: layout | None = None, pin_memory: bool | None = False, requires_grad: bool | None = False) → Tensor2D
torchwrench.nn.functional.new.empty(size: tuple[int, int, int], /, *, dtype: dtype | None | 'default' | str | DTypeEnum = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None, memory_format: memory_format | None = None, out: Tensor | None = None, layout: layout | None = None, pin_memory: bool | None = False, requires_grad: bool | None = False) → Tensor3D
torchwrench.nn.functional.new.empty(size0: int, /, *, dtype: dtype | None | 'default' | str | DTypeEnum = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None, memory_format: memory_format | None = None, out: Tensor | None = None, layout: layout | None = None, pin_memory: bool | None = False, requires_grad: bool | None = False) → Tensor1D
torchwrench.nn.functional.new.empty(size0: int, size1: int, /, *, dtype: dtype | None | 'default' | str | DTypeEnum = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None, memory_format: memory_format | None = None, out: Tensor | None = None, layout: layout | None = None, pin_memory: bool | None = False, requires_grad: bool | None = False) → Tensor2D
torchwrench.nn.functional.new.empty(size0: int, size1: int, size2: int, /, *, dtype: dtype | None | 'default' | str | DTypeEnum = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None, memory_format: memory_format | None = None, out: Tensor | None = None, layout: layout | None = None, pin_memory: bool | None = False, requires_grad: bool | None = False) → Tensor3D
torchwrench.nn.functional.new.full(size: Sequence[Never], fill_value: bool, *, dtype: None = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None, out: Tensor | None = None, layout: layout | None = None, pin_memory: bool | None = False, requires_grad: bool | None = False) → BoolTensor0D[source]
torchwrench.nn.functional.new.full(size: tuple[int], fill_value: bool, *, dtype: None = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None, out: Tensor | None = None, layout: layout | None = None, pin_memory: bool | None = False, requires_grad: bool | None = False) → BoolTensor1D
torchwrench.nn.functional.new.full(size: tuple[int, int], fill_value: bool, *, dtype: None = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None, out: Tensor | None = None, layout: layout | None = None, pin_memory: bool | None = False, requires_grad: bool | None = False) → BoolTensor2D
torchwrench.nn.functional.new.full(size: tuple[int, int, int], fill_value: bool, *, dtype: None = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None, out: Tensor | None = None, layout: layout | None = None, pin_memory: bool | None = False, requires_grad: bool | None = False) → BoolTensor3D
torchwrench.nn.functional.new.full(size: Sequence[Never], fill_value: int, *, dtype: None = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None, out: Tensor | None = None, layout: layout | None = None, pin_memory: bool | None = False, requires_grad: bool | None = False) → LongTensor0D
torchwrench.nn.functional.new.full(size: tuple[int], fill_value: int, *, dtype: None = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None, out: Tensor | None = None, layout: layout | None = None, pin_memory: bool | None = False, requires_grad: bool | None = False) → LongTensor1D
torchwrench.nn.functional.new.full(size: tuple[int, int], fill_value: int, *, dtype: None = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None, out: Tensor | None = None, layout: layout | None = None, pin_memory: bool | None = False, requires_grad: bool | None = False) → LongTensor2D
torchwrench.nn.functional.new.full(size: tuple[int, int, int], fill_value: int, *, dtype: None = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None, out: Tensor | None = None, layout: layout | None = None, pin_memory: bool | None = False, requires_grad: bool | None = False) → LongTensor3D
torchwrench.nn.functional.new.full(size: Sequence[Never], fill_value: float, *, dtype: None = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None, out: Tensor | None = None, layout: layout | None = None, pin_memory: bool | None = False, requires_grad: bool | None = False) → FloatTensor0D
torchwrench.nn.functional.new.full(size: tuple[int], fill_value: float, *, dtype: None = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None, out: Tensor | None = None, layout: layout | None = None, pin_memory: bool | None = False, requires_grad: bool | None = False) → FloatTensor1D
torchwrench.nn.functional.new.full(size: tuple[int, int], fill_value: float, *, dtype: None = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None, out: Tensor | None = None, layout: layout | None = None, pin_memory: bool | None = False, requires_grad: bool | None = False) → FloatTensor2D
torchwrench.nn.functional.new.full(size: tuple[int, int, int], fill_value: float, *, dtype: None = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None, out: Tensor | None = None, layout: layout | None = None, pin_memory: bool | None = False, requires_grad: bool | None = False) → FloatTensor3D
torchwrench.nn.functional.new.full(size: Sequence[Never], fill_value: complex, *, dtype: None = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None, out: Tensor | None = None, layout: layout | None = None, pin_memory: bool | None = False, requires_grad: bool | None = False) → CFloatTensor0D
torchwrench.nn.functional.new.full(size: tuple[int], fill_value: complex, *, dtype: None = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None, out: Tensor | None = None, layout: layout | None = None, pin_memory: bool | None = False, requires_grad: bool | None = False) → CFloatTensor1D
torchwrench.nn.functional.new.full(size: tuple[int, int], fill_value: complex, *, dtype: None = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None, out: Tensor | None = None, layout: layout | None = None, pin_memory: bool | None = False, requires_grad: bool | None = False) → CFloatTensor2D
torchwrench.nn.functional.new.full(size: tuple[int, int, int], fill_value: complex, *, dtype: None = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None, out: Tensor | None = None, layout: layout | None = None, pin_memory: bool | None = False, requires_grad: bool | None = False) → CFloatTensor3D
torchwrench.nn.functional.new.full(size: Sequence[Never], fill_value: bool | int | float | complex, *, dtype: dtype | None | 'default' | str | DTypeEnum = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None, out: Tensor | None = None, layout: layout | None = None, pin_memory: bool | None = False, requires_grad: bool | None = False) → Tensor0D
torchwrench.nn.functional.new.full(size: tuple[int], fill_value: bool | int | float | complex, *, dtype: dtype | None | 'default' | str | DTypeEnum = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None, out: Tensor | None = None, layout: layout | None = None, pin_memory: bool | None = False, requires_grad: bool | None = False) → Tensor1D
torchwrench.nn.functional.new.full(size: tuple[int, int], fill_value: bool | int | float | complex, *, dtype: dtype | None | 'default' | str | DTypeEnum = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None, out: Tensor | None = None, layout: layout | None = None, pin_memory: bool | None = False, requires_grad: bool | None = False) → Tensor2D
torchwrench.nn.functional.new.full(size: tuple[int, int, int], fill_value: bool | int | float | complex, *, dtype: dtype | None | 'default' | str | DTypeEnum = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None, out: Tensor | None = None, layout: layout | None = None, pin_memory: bool | None = False, requires_grad: bool | None = False) → Tensor3D
torchwrench.nn.functional.new.ones(size: Sequence[Never], /, *, dtype: dtype | None | 'default' | str | DTypeEnum = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None, out: Tensor | None = None, layout: layout | None = None, pin_memory: bool | None = False, requires_grad: bool | None = False) → Tensor0D[source]
torchwrench.nn.functional.new.ones(size: tuple[int], /, *, dtype: dtype | None | 'default' | str | DTypeEnum = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None, out: Tensor | None = None, layout: layout | None = None, pin_memory: bool | None = False, requires_grad: bool | None = False) → Tensor1D
torchwrench.nn.functional.new.ones(size: tuple[int, int], /, *, dtype: dtype | None | 'default' | str | DTypeEnum = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None, out: Tensor | None = None, layout: layout | None = None, pin_memory: bool | None = False, requires_grad: bool | None = False) → Tensor2D
torchwrench.nn.functional.new.ones(size: tuple[int, int, int], /, *, dtype: dtype | None | 'default' | str | DTypeEnum = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None, out: Tensor | None = None, layout: layout | None = None, pin_memory: bool | None = False, requires_grad: bool | None = False) → Tensor3D
torchwrench.nn.functional.new.ones(size0: int, /, *, dtype: dtype | None | 'default' | str | DTypeEnum = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None, out: Tensor | None = None, layout: layout | None = None, pin_memory: bool | None = False, requires_grad: bool | None = False) → Tensor1D
torchwrench.nn.functional.new.ones(size0: int, size1: int, /, *, dtype: dtype | None | 'default' | str | DTypeEnum = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None, out: Tensor | None = None, layout: layout | None = None, pin_memory: bool | None = False, requires_grad: bool | None = False) → Tensor2D
torchwrench.nn.functional.new.ones(size0: int, size1: int, size2: int, /, *, dtype: dtype | None | 'default' | str | DTypeEnum = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None, out: Tensor | None = None, layout: layout | None = None, pin_memory: bool | None = False, requires_grad: bool | None = False) → Tensor3D
torchwrench.nn.functional.new.rand(size: Sequence[Never], /, *, dtype: 'float' | 'float32' | None = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None, generator: Generator | None | 'default' | int = None, out: Tensor | None = None, layout: layout | None = None, pin_memory: bool | None = False, requires_grad: bool | None = False) → FloatTensor0D[source]
torchwrench.nn.functional.new.rand(size: tuple[int], /, *, dtype: 'float' | 'float32' | None = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None, generator: Generator | None | 'default' | int = None, out: Tensor | None = None, layout: layout | None = None, pin_memory: bool | None = False, requires_grad: bool | None = False) → FloatTensor1D
torchwrench.nn.functional.new.rand(size: tuple[int, int], /, *, dtype: 'float' | 'float32' | None = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None, generator: Generator | None | 'default' | int = None, out: Tensor | None = None, layout: layout | None = None, pin_memory: bool | None = False, requires_grad: bool | None = False) → FloatTensor2D
torchwrench.nn.functional.new.rand(size: tuple[int, int, int], /, *, dtype: 'float' | 'float32' | None = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None, generator: Generator | None | 'default' | int = None, out: Tensor | None = None, layout: layout | None = None, pin_memory: bool | None = False, requires_grad: bool | None = False) → FloatTensor3D
torchwrench.nn.functional.new.rand(size0: int, /, *, dtype: 'float' | 'float32' | None = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None, generator: Generator | None | 'default' | int = None, out: Tensor | None = None, layout: layout | None = None, pin_memory: bool | None = False, requires_grad: bool | None = False) → FloatTensor1D
torchwrench.nn.functional.new.rand(size0: int, size1: int, /, *, dtype: 'float' | 'float32' | None = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None, generator: Generator | None | 'default' | int = None, out: Tensor | None = None, layout: layout | None = None, pin_memory: bool | None = False, requires_grad: bool | None = False) → FloatTensor2D
torchwrench.nn.functional.new.rand(size0: int, size1: int, size2: int, /, *, dtype: 'float' | 'float32' | None = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None, generator: Generator | None | 'default' | int = None, out: Tensor | None = None, layout: layout | None = None, pin_memory: bool | None = False, requires_grad: bool | None = False) → FloatTensor3D
torchwrench.nn.functional.new.rand(size: Sequence[Never], /, *, dtype: dtype | None | 'default' | str | DTypeEnum, device: device | None | 'default' | 'cuda_if_available' | str | int = None, generator: Generator | None | 'default' | int = None, out: Tensor | None = None, layout: layout | None = None, pin_memory: bool | None = False, requires_grad: bool | None = False) → Tensor0D
torchwrench.nn.functional.new.rand(size: tuple[int], /, *, dtype: dtype | None | 'default' | str | DTypeEnum, device: device | None | 'default' | 'cuda_if_available' | str | int = None, generator: Generator | None | 'default' | int = None, out: Tensor | None = None, layout: layout | None = None, pin_memory: bool | None = False, requires_grad: bool | None = False) → Tensor1D
torchwrench.nn.functional.new.rand(size: tuple[int, int], /, *, dtype: dtype | None | 'default' | str | DTypeEnum, device: device | None | 'default' | 'cuda_if_available' | str | int = None, generator: Generator | None | 'default' | int = None, out: Tensor | None = None, layout: layout | None = None, pin_memory: bool | None = False, requires_grad: bool | None = False) → Tensor2D
torchwrench.nn.functional.new.rand(size: tuple[int, int, int], /, *, dtype: dtype | None | 'default' | str | DTypeEnum, device: device | None | 'default' | 'cuda_if_available' | str | int = None, generator: Generator | None | 'default' | int = None, out: Tensor | None = None, layout: layout | None = None, pin_memory: bool | None = False, requires_grad: bool | None = False) → Tensor3D
torchwrench.nn.functional.new.rand(size0: int, /, *, dtype: dtype | None | 'default' | str | DTypeEnum, device: device | None | 'default' | 'cuda_if_available' | str | int = None, generator: Generator | None | 'default' | int = None, out: Tensor | None = None, layout: layout | None = None, pin_memory: bool | None = False, requires_grad: bool | None = False) → Tensor1D
torchwrench.nn.functional.new.rand(size0: int, size1: int, /, *, dtype: dtype | None | 'default' | str | DTypeEnum, device: device | None | 'default' | 'cuda_if_available' | str | int = None, generator: Generator | None | 'default' | int = None, out: Tensor | None = None, layout: layout | None = None, pin_memory: bool | None = False, requires_grad: bool | None = False) → Tensor2D
torchwrench.nn.functional.new.rand(size0: int, size1: int, size2: int, /, *, dtype: dtype | None | 'default' | str | DTypeEnum, device: device | None | 'default' | 'cuda_if_available' | str | int = None, generator: Generator | None | 'default' | int = None, out: Tensor | None = None, layout: layout | None = None, pin_memory: bool | None = False, requires_grad: bool | None = False) → Tensor3D
torchwrench.nn.functional.new.randint(low: int, high: int, size: tuple, *, generator: Generator | None | 'default' | int = None, dtype: None | 'long' | 'int64' = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None, requires_grad: bool = False, pin_memory: bool = False) → LongTensor0D[source]
torchwrench.nn.functional.new.randint(low: int, high: int, size: tuple[int], *, generator: Generator | None | 'default' | int = None, dtype: None | 'long' | 'int64' = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None, requires_grad: bool = False, pin_memory: bool = False) → LongTensor1D
torchwrench.nn.functional.new.randint(low: int, high: int, size: tuple[int, int], *, generator: Generator | None | 'default' | int = None, dtype: None | 'long' | 'int64' = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None, requires_grad: bool = False, pin_memory: bool = False) → LongTensor2D
torchwrench.nn.functional.new.randint(low: int, high: int, size: tuple[int, int, int], *, generator: Generator | None | 'default' | int = None, dtype: None | 'long' | 'int64' = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None, requires_grad: bool = False, pin_memory: bool = False) → LongTensor3D
torchwrench.nn.functional.new.randint(low: int, high: int, size: tuple, *, generator: Generator | None | 'default' | int = None, dtype: dtype | str | None | 'default' | DTypeEnum, device: device | None | 'default' | 'cuda_if_available' | str | int = None, requires_grad: bool = False, pin_memory: bool = False) → Tensor0D
torchwrench.nn.functional.new.randint(low: int, high: int, size: tuple[int], *, generator: Generator | None | 'default' | int = None, dtype: dtype | str | None | 'default' | DTypeEnum, device: device | None | 'default' | 'cuda_if_available' | str | int = None, requires_grad: bool = False, pin_memory: bool = False) → Tensor1D
torchwrench.nn.functional.new.randint(low: int, high: int, size: tuple[int, int], *, generator: Generator | None | 'default' | int = None, dtype: dtype | str | None | 'default' | DTypeEnum, device: device | None | 'default' | 'cuda_if_available' | str | int = None, requires_grad: bool = False, pin_memory: bool = False) → Tensor2D
torchwrench.nn.functional.new.randint(low: int, high: int, size: tuple[int, int, int], *, generator: Generator | None | 'default' | int = None, dtype: dtype | str | None | 'default' | DTypeEnum, device: device | None | 'default' | 'cuda_if_available' | str | int = None, requires_grad: bool = False, pin_memory: bool = False) → Tensor3D
torchwrench.nn.functional.new.randn(size: Sequence[int], *, generator: Generator | None | 'default' | int = None, dtype: dtype | None | 'default' | str | DTypeEnum = None, layout: layout | None = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None, pin_memory: bool | None = False, requires_grad: bool | None = False) → Tensor[source]
torchwrench.nn.functional.new.randperm(n: int, *, generator: Generator | None | 'default' | int = None, out: Tensor | None = None, dtype: None | 'long' | 'int64' = 'long', layout: layout | None = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None, pin_memory: bool | None = False, requires_grad: bool | None = False) → LongTensor1D[source]
torchwrench.nn.functional.new.randperm(n: int, *, generator: Generator | None | 'default' | int = None, out: Tensor | None = None, dtype: dtype | str | None | 'default' | DTypeEnum, layout: layout | None = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None, pin_memory: bool | None = False, requires_grad: bool | None = False) → Tensor1D
torchwrench.nn.functional.new.zeros(size: Sequence[Never], /, *, dtype: dtype | None | 'default' | str | DTypeEnum = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None, out: Tensor | None = None, layout: layout | None = None, pin_memory: bool | None = False, requires_grad: bool | None = False) → Tensor0D[source]
torchwrench.nn.functional.new.zeros(size: tuple[int], /, *, dtype: dtype | None | 'default' | str | DTypeEnum = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None, out: Tensor | None = None, layout: layout | None = None, pin_memory: bool | None = False, requires_grad: bool | None = False) → Tensor1D
torchwrench.nn.functional.new.zeros(size: tuple[int, int], /, *, dtype: dtype | None | 'default' | str | DTypeEnum = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None, out: Tensor | None = None, layout: layout | None = None, pin_memory: bool | None = False, requires_grad: bool | None = False) → Tensor2D
torchwrench.nn.functional.new.zeros(size: tuple[int, int, int], /, *, dtype: dtype | None | 'default' | str | DTypeEnum = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None, out: Tensor | None = None, layout: layout | None = None, pin_memory: bool | None = False, requires_grad: bool | None = False) → Tensor3D
torchwrench.nn.functional.new.zeros(size0: int, /, *, dtype: dtype | None | 'default' | str | DTypeEnum = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None, out: Tensor | None = None, layout: layout | None = None, pin_memory: bool | None = False, requires_grad: bool | None = False) → Tensor1D
torchwrench.nn.functional.new.zeros(size0: int, size1: int, /, *, dtype: dtype | None | 'default' | str | DTypeEnum = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None, out: Tensor | None = None, layout: layout | None = None, pin_memory: bool | None = False, requires_grad: bool | None = False) → Tensor2D
torchwrench.nn.functional.new.zeros(size0: int, size1: int, size2: int, /, *, dtype: dtype | None | 'default' | str | DTypeEnum = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None, out: Tensor | None = None, layout: layout | None = None, pin_memory: bool | None = False, requires_grad: bool | None = False) → Tensor3D