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.


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.


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.


>>> 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

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


Access comprehensive developer documentation for PyTorch

View Docs


Get in-depth tutorials for beginners and advanced developers

View Tutorials


Find development resources and get your questions answered

View Resources