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# torch.Tensor.to

Tensor.to(*args, **kwargs) → Tensor

  Performs Tensor dtype and/or device conversion. A `torch.dtype` and `torch.device` are inferred from the arguments of `self.to(*args, **kwargs)`.

  Note

  If the `self` Tensor already has the correct `torch.dtype` and `torch.device`, then `self` is returned. Otherwise, the returned tensor is a copy of `self` with the desired `torch.dtype` and `torch.device`.

  Here are the ways to call `to`:

  to(dtype, non_blocking=False, copy=False, memory_format=torch.preserve_format) → Tensor

    > Returns a Tensor with the specified `dtype`

    > Args:

    >   memory_format (`torch.memory_format`, optional): the desired memory format of returned Tensor. Default: `torch.preserve_format`.

  torch.to(device=None, dtype=None, non_blocking=False, copy=False, memory_format=torch.preserve_format) → Tensor

    > Returns a Tensor with the specified `device` and (optional) `dtype`. If `dtype` is `None` it is inferred to be `self.dtype`. When `non_blocking`, tries to convert asynchronously with respect to the host if possible, e.g., converting a CPU Tensor with pinned memory to a CUDA Tensor. When `copy` is set, a new Tensor is created even when the Tensor already matches the desired conversion.

    > Args:

    >   memory_format (`torch.memory_format`, optional): the desired memory format of returned Tensor. Default: `torch.preserve_format`.

  torch.to(other, non_blocking=False, copy=False) → Tensor

    > Returns a Tensor with same `torch.dtype` and `torch.device` as the Tensor `other`. When `non_blocking`, tries to convert asynchronously with respect to the host if possible, e.g., converting a CPU Tensor with pinned memory to a CUDA Tensor. When `copy` is set, a new Tensor is created even when the Tensor already matches the desired conversion.

  Example:

      >>> tensor = torch.randn(2, 2)  # Initially dtype=float32, device=cpu
      >>> tensor.to(torch.float64)
      tensor([[-0.5044,  0.0005],
              [ 0.3310, -0.0584]], dtype=torch.float64)

      >>> cuda0 = torch.device('cuda:0')
      >>> tensor.to(cuda0)
      tensor([[-0.5044,  0.0005],
              [ 0.3310, -0.0584]], device='cuda:0')

      >>> tensor.to(cuda0, dtype=torch.float64)
      tensor([[-0.5044,  0.0005],
              [ 0.3310, -0.0584]], dtype=torch.float64, device='cuda:0')

      >>> other = torch.randn((), dtype=torch.float64, device=cuda0)
      >>> tensor.to(other, non_blocking=True)
      tensor([[-0.5044,  0.0005],
              [ 0.3310, -0.0584]], dtype=torch.float64, device='cuda:0')

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