| from typing_extensions import TypeIs | |
| from torch import device, dtype, Tensor | |
| class Parameter(Tensor): | |
| def __init__(self, data: Tensor = ..., requires_grad: bool = ...) -> None: ... | |
| def is_lazy( | |
| param: Tensor, | |
| ) -> TypeIs[UninitializedParameter | UninitializedBuffer]: ... | |
| class UninitializedParameter(Tensor): | |
| def __init__(self, data: Tensor = ..., requires_grad: bool = ...) -> None: ... | |
| def materialize( | |
| self, | |
| shape: tuple[int, ...], | |
| device: device | None = None, | |
| dtype: dtype | None = None, | |
| ) -> None: ... | |
| class Buffer(Tensor): | |
| persistent: bool | |
| def __init__( | |
| self, | |
| data: Tensor = ..., | |
| requires_grad: bool = ..., | |
| persistent: bool = ..., | |
| ): ... | |
| class UninitializedBuffer(Tensor): | |
| persistent: bool | |
| def __init__( | |
| self, | |
| data: Tensor = ..., | |
| requires_grad: bool = ..., | |
| persistent: bool = ..., | |
| ): ... | |
| def materialize( | |
| self, | |
| shape: tuple[int, ...], | |
| device: device | None = None, | |
| dtype: dtype | None = None, | |
| ) -> None: ... | |