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# torch.as_tensor

torch.as_tensor(data, dtype=None, device=None) → Tensor

  Converts `data` into a tensor, sharing data and preserving autograd history if possible.

  If `data` is already a tensor with the requested dtype and device then `data` itself is returned, but if `data` is a tensor with a different dtype or device then it’s copied as if using data.to(dtype=dtype, device=device).

  If `data` is a NumPy array (an ndarray) with the same dtype and device then a tensor is constructed using `torch.from_numpy()`.

  See also

  `torch.tensor()` never shares its data and creates a new “leaf tensor” (see Autograd mechanics).

  Parameters:

    - data (array_like) – Initial data for the tensor. Can be a list, tuple, NumPy `ndarray`, scalar, and other types.

    - dtype (`torch.dtype`, optional) – the desired data type of returned tensor. Default: if `None`, infers data type from `data`.

    - device (`torch.device`, optional) – the device of the constructed tensor. If None and data is a tensor then the device of data is used. If None and data is not a tensor then the result tensor is constructed on the current device.

  Example:

      >>> a = numpy.array([1, 2, 3])
      >>> t = torch.as_tensor(a)
      >>> t
      tensor([ 1,  2,  3])
      >>> t[0] = -1
      >>> a
      array([-1,  2,  3])

      >>> a = numpy.array([1, 2, 3])
      >>> t = torch.as_tensor(a, device=torch.device('cuda'))
      >>> t
      tensor([ 1,  2,  3])
      >>> t[0] = -1
      >>> a
      array([1,  2,  3])

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