# Copyright 2024 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# ============================================================================
"""mint module."""
from __future__ import absolute_import
from mindspore.ops.auto_generate import erfc, expm1, sinc, log1p, round_op
from mindspore.ops.function.nn_func import log_softmax_ext as log_softmax
[文档]def round(input):
r"""
Returns half to even of a tensor element-wise.
.. math::
out_i \approx input_i
.. note::
The input data types supported by the Ascend platform include
bfloat16 (Atlas training series products are not supported), float16, float32, float64, int32, and int64.
Args:
input (Tensor): The input tensor.
Returns:
Tensor, has the same shape and type as the `input`.
Raises:
TypeError: If `input` is not a Tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> input = Tensor(np.array([0.8, 1.5, 2.3, 2.5, -4.5]), mindspore.float32)
>>> output = mint.special.round(input)
>>> print(output)
[ 1. 2. 2. 2. -4.]
"""
return round_op(input, None)
__all__ = [
'erfc',
'expm1',
'round',
'sinc',
'log1p',
'log_softmax',
]