- torch.empty(*size, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, pin_memory=False, memory_format=torch.contiguous_format) Tensor ¶
Returns a tensor filled with uninitialized data. The shape of the tensor is defined by the variable argument
torch.use_deterministic_algorithms()is set to
True, the output tensor is initialized to prevent any possible nondeterministic behavior from using the data as an input to an operation. Floating point and complex tensors are filled with NaN, and integer tensors are filled with the maximum value.
size (int...) – a sequence of integers defining the shape of the output tensor. Can be a variable number of arguments or a collection like a list or tuple.
- Keyword Arguments
out (Tensor, optional) – the output tensor.
torch.layout, optional) – the desired layout of returned Tensor. Default:
torch.device, optional) – the desired device of returned tensor. Default: if
None, uses the current device for the default tensor type (see
devicewill be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types.
requires_grad (bool, optional) – If autograd should record operations on the returned tensor. Default:
pin_memory (bool, optional) – If set, returned tensor would be allocated in the pinned memory. Works only for CPU tensors. Default:
torch.memory_format, optional) – the desired memory format of returned Tensor. Default:
>>> torch.empty((2,3), dtype=torch.int64) tensor([[ 9.4064e+13, 2.8000e+01, 9.3493e+13], [ 7.5751e+18, 7.1428e+18, 7.5955e+18]])