text: # Copyright 2020 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.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ============================================================================
  """Power Bijector"""
  from .power_transform import PowerTransform
  
  
  class Exp(PowerTransform):
      r"""
      Exponential Bijector.
      This Bijector performs the operation:
  
      .. math::
          Y = \exp(x).
  
      Args:
          name (str): The name of the Bijector. Default: 'Exp'.
  
      Supported Platforms:
          ``Ascend`` ``GPU``
  
      Examples:
          >>> import mindspore
          >>> import mindspore.nn as nn
          >>> from mindspore import Tensor
          >>>
          >>> # To initialize an Exp bijector.
          >>> exp_bijector = nn.probability.bijector.Exp()
          >>> value = Tensor([1, 2, 3], dtype=mindspore.float32)
          >>> ans1 = exp_bijector.forward(value)
          >>> print(ans1.shape)
          (3,)
          >>> ans2 = exp_bijector.inverse(value)
          >>> print(ans2.shape)
          (3,)
          >>> ans3 = exp_bijector.forward_log_jacobian(value)
          >>> print(ans3.shape)
          (3,)
          >>> ans4 = exp_bijector.inverse_log_jacobian(value)
          >>> print(ans4.shape)
          (3,)
      """
  
      def __init__(self,
                   name='Exp'):
          super(Exp, self).__init__(name=name)
  
      def extend_repr(self):
          if self.is_scalar_batch:
              str_info = 'exp'
          else:
              str_info = f'batch_shape = {self.batch_shape}'
          return str_info
  