mindspore.mint.empty_like

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

Returns an uninitialized Tensor with the same shape as the input. Its dtype is specified by dtype and its device is specified by device. If pin_memory is True, the tensor will be allocated in pinned memory.

Parameters

input (Tensor) – Tensor of any dimension.

Keyword Arguments

  • dtype (mindspore.dtype, optional) – The specified dtype of the output tensor. If dtype = None, the tensor will have the same dtype as input input. 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, the value set by mindspore.set_device() 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, has the same shape, type and device as input but with uninitialized data (May be a random value).

Raises

  • TypeError – If input is not a Tensor.

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

Supported Platforms:

Ascend

Examples

>>> import mindspore
>>> from mindspore import mint, Tensor
>>> x = Tensor([[1, 2, 3], [4, 5, 6]])
>>> output1 = mint.empty_like(x)
>>> print(output1)
[[0 0 0]
 [0 0 0]]
>>> output2 = mint.empty_like(x, dtype=mindspore.float64)
>>> print(output2)
[[0. 0. 0.]
 [0. 0. 0.]]