torch.tensor — PyTorch 2.7 documentation (original) (raw)

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.

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([])