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# torch.tensor

torch.tensor(data, *, dtype=None, device=None, requires_grad=False, pin_memory=False) → Tensor

  Constructs a tensor with no autograd history (also known as a “leaf tensor”, see Autograd mechanics) by copying `data`.

  Warning

  When working with tensors prefer using `torch.Tensor.clone()`, `torch.Tensor.detach()`, and `torch.Tensor.requires_grad_()` for readability. Letting t be a tensor, `torch.tensor(t)` is equivalent to `t.clone().detach()`, and `torch.tensor(t, requires_grad=True)` is equivalent to `t.clone().detach().requires_grad_(True)`.

  See also

  `torch.as_tensor()` preserves autograd history and avoids copies where possible. `torch.from_numpy()` creates a tensor that shares storage with a NumPy array.

  Parameters:

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

  Keyword Arguments:

    - 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.

    - 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:

      >>> torch.tensor([[0.1, 1.2], [2.2, 3.1], [4.9, 5.2]])
      tensor([[ 0.1000,  1.2000],
              [ 2.2000,  3.1000],
              [ 4.9000,  5.2000]])

      >>> torch.tensor([0, 1])  # Type inference on data
      tensor([ 0,  1])

      >>> torch.tensor([[0.11111, 0.222222, 0.3333333]],
      ...              dtype=torch.float64,
      ...              device=torch.device('cuda:0'))  # creates a double tensor on a CUDA device
      tensor([[ 0.1111,  0.2222,  0.3333]], dtype=torch.float64, device='cuda:0')

      >>> torch.tensor(3.14159)  # Create a zero-dimensional (scalar) tensor
      tensor(3.1416)

      >>> torch.tensor([])  # Create an empty tensor (of size (0,))
      tensor([])

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