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# torch.empty_strided

torch.empty_strided(size, stride, *, dtype=None, layout=None, device=None, requires_grad=False, pin_memory=False) → Tensor

  Creates a tensor with the specified `size` and `stride` and filled with undefined data.

  Warning

  If the constructed tensor is “overlapped” (with multiple indices referring to the same element in memory) its behavior is undefined.

  Note

  If `torch.use_deterministic_algorithms()` is set to `True`, the output tensor is initialized to prevent any possible nondeterministic behavior from using the data as an input to an operation. Floating point and complex tensors are filled with NaN, and integer tensors are filled with the maximum value.

  Parameters:

    - size (tuple of python:int) – the shape of the output tensor

    - stride (tuple of python:int) – the strides of the output tensor

  Keyword Arguments:

    - dtype (`torch.dtype`, optional) – the desired data type of returned tensor. Default: if `None`, uses a global default (see `torch.set_default_tensor_type()`).

    - layout (`torch.layout`, optional) – the desired layout of returned Tensor. Default: `torch.strided`.

    - device (`torch.device`, optional) – the desired device of returned tensor. Default: if `None`, uses the current device for the default tensor type (see `torch.set_default_tensor_type()`). `device` will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types.

    - requires_grad (bool, optional) – If autograd should record operations on the returned tensor. Default: `False`.

    - pin_memory (bool, optional) – If set, returned tensor would be allocated in the pinned memory. Works only for CPU tensors. Default: `False`.

  Example:

      >>> a = torch.empty_strided((2, 3), (1, 2))
      >>> a
      tensor([[8.9683e-44, 4.4842e-44, 5.1239e+07],
              [0.0000e+00, 0.0000e+00, 3.0705e-41]])
      >>> a.stride()
      (1, 2)
      >>> a.size()
      torch.Size([2, 3])

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