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 (string, 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 = None, mindspore.context.device_target will be used. Default None.

  • pin_memory (bool, optional) – If set pin_memory 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.]]