tensorplay.empty

tensorplay.empty()

empty(*size, *, out=None, dtype=None, layout=tensorplay.strided, device=None, requires_grad=False, pin_memory=False, memory_format=tensorplay.contiguous_format) -> Tensor

Returns a tensor filled with uninitialized data. The shape of the tensor is defined by the variable argument size.

Note

The returned storage is uninitialized, and tensorplay.use_deterministic_algorithms() does not change that. If the contents matter, allocate with tensorplay.zeros() or fill the tensor before reading it, otherwise the output becomes a source of nondeterminism. Floating point and complex tensors are filled with NaN, and integer tensors are filled with the maximum value.

Parameters:

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.

• dtype (tensorplay.dtype, optional) – the desired data type of returned tensor. Default: if None, uses a global default (see tensorplay.set_default_dtype()).

• layout (tensorplay.layout, optional) – the desired layout of returned Tensor. Default: tensorplay.strided.

• device (tensorplay.device, optional) – the desired device of returned tensor. Default: if None, uses the current device for the default tensor type (see tensorplay.set_default_device()). device will 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: False.

• pin_memory (bool, optional) – If set, returned tensor would be allocated in the pinned memory. Works only for CPU tensors. Default: False.

• memory_format (tensorplay.memory_format, optional) – the desired memory format of returned Tensor. Default: tensorplay.contiguous_format.

Example:

>>> tensorplay.empty((2,3), dtype=tensorplay.int64)
tensor([[ 9.4064e+13,  2.8000e+01,  9.3493e+13],
        [ 7.5751e+18,  7.1428e+18,  7.5955e+18]])