mindspore.mint.empty

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mindspore.mint.empty(*size, *, dtype=None, device=None, pin_memory=False) → Tensor[source]

Creates a tensor with uninitialized data, whose shape, dtype and device are described by the argument size, dtype and device respectively. If pin_memory is True, the tensor will be allocated in pinned memory.

Parameters

size (Union[tuple[int], list[int], int]) – The specified shape of output tensor. Can be variable numbers of positive integers or tuple or list containing positive integers.

Keyword Arguments

  • dtype (mindspore.dtype, optional) – The specified type of output tensor. If dtype is None , mindspore.float32 will be used. Default: None .

  • device (str, optional) – The specified device of the output tensor. In PyNative mode, "Ascend", "npu", "cpu" and "CPU" are supported. In graph mode O0, "Ascend" and "npu" are supported. If device is None, mindspore.context.device_target will be used. Default None.

  • pin_memory (bool, optional) – If set to True, the tensor will be allocated in pinned memory, and device should be "cpu" or "CPU" . Default False.

Returns

Tensor, whose shape, dtype and device are defined by input.

Raises

  • TypeError – If size is neither an int nor a tuple or list of int.

  • RuntimeError – If pin_memory is True, and device is neither "cpu" nor "CPU" .

Supported Platforms:

Ascend

Examples

>>> import mindspore
>>> from mindspore import mint
>>> output = mint.empty((2, 3), dtype=mindspore.float32)
>>> print(output)
[[0. 0. 0.]
 [0. 0. 0.]]