text: #   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
  #
  # 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.
  
  from ...fluid.layers import sigmoid  # noqa: F401
  from ...tensor.math import tanh  # noqa: F401
  from ...tensor.math import tanh_  # noqa: F401
  
  from ...fluid.dygraph.inplace_utils import inplace_apis_in_dygraph_only
  from ...tensor.manipulation import chunk
  from ...tensor.math import multiply
  
  import warnings
  from ...fluid.layer_helper import LayerHelper
  from ...fluid.framework import convert_np_dtype_to_dtype_
  from ...fluid.framework import _in_legacy_dygraph, in_dygraph_mode, _non_static_mode
  from ...fluid.data_feeder import check_variable_and_dtype, check_dtype
  import paddle
  from paddle import _C_ops, in_dynamic_mode
  from paddle.framework import core
  
  __all__ = []
  
  
  def celu(x, alpha=1.0, name=None):
      r"""
      celu activation.
  
      .. math::
  
          celu(x) = max(0, x) + min(0, \alpha * (e^{x/\alpha}-1))
  
      Parameters:
          x (Tensor): The input Tensor with data type float32, float64.
          alpha (float, optional): The 'alpha' value of the CELU formulation. Default is 1.0.
          name (str, optional): Name for the operation (optional, default is None).
              For more information, please refer to :ref:`api_guide_Name`.
  
      Returns:
          A Tensor with the same data type and shape as ``x`` .
  
      Examples:
          .. code-block:: python
  
              import paddle
              import paddle.nn.functional as F
              x = paddle.to_tensor([[-1., 6.], [1., 15.6]])
              out = F.celu(x, alpha=0.2)
              # [[-0.19865242,  6.        ],
              #  [ 1.        , 15.60000038]]
      """
      if alpha == 0:
          raise ZeroDivisionError("alpha cannot be 0 for celu")
  
      if in_dynamic_mode():
          return _C_ops.celu(x, 'alpha', alpha)
  
      check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'celu')
      helper = LayerHelper("celu", **locals())
      out = helper.create_variable_for_type_inference(x.dtype)
      helper.append_op(
          type='celu',
          inputs={'X': x},
          outputs={'Out': out},
          attrs={'alpha': alpha})
      return out
  
  
  def elu(x, alpha=1.0, name=None):
      r"""
      elu activation.
  
      .. math::
  
          elu(x)=
              \left\{
                  \begin{array}{lcl}
                  x,& &\text{if } \ x > 0 \\
                  alpha * (e^{x} - 1),& &\text{if } \ x <= 0
                  \end{array}
              \right.
  
      Parameters:
          x (Tensor): The input Tensor with data type float32, float64.
          alpha (float, optional): The 'alpha' value of the ELU formulation. Default is 1.0.
          name (str, optional): Name for the operation (optional, default is None).
              For more information, please refer to :ref:`api_guide_Name`.
  
      Returns:
          A Tensor with the same data type and shape as ``x`` .
  
      Examples:
          .. code-block:: python
  
              import paddle
              import paddle.nn.functional as F
  
              x = paddle.to_tensor([[-1., 6.], [1., 15.6]])
              out = F.elu(x, alpha=0.2)
              # [[-0.12642411  6.        ]
              #  [ 1.          15.6      ]]
      """
  
      if in_dynamic_mode():
          return _C_ops.elu(x, 'alpha', alpha)
  
      check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'elu')
      helper = LayerHelper("elu", **locals())
      out = helper.create_variable_for_type_inference(x.dtype)
      helper.append_op(
          type='elu',
          inputs={'X': x},
          outputs={'Out': out},
          attrs={'alpha': alpha})
      return out
  
  
  @inplace_apis_in_dygraph_only
  def elu_(x, alpha=1.0, name=None):
      r"""
      Inplace version of ``elu`` API, the output Tensor will be inplaced with input ``x``.
      Please refer to :ref:`api_nn_cn_elu`.
      """
      assert alpha >= 0., "elu_ only support alpha >= 0, please use elu instead."
      return _C_ops.elu_(x, 'alpha', alpha)
  
  
  def gelu(x, approximate=False, name=None):
      r"""
      gelu activation.
  
      if approximate is True
  
      .. math::
  
          gelu(x) = 0.5 * x * (1 + tanh(\sqrt{\frac{2}{\pi}} * (x + 0.044715x^{3})))
  
      else
  
      .. math::
  
          gelu(x) = 0.5 * x * (1 + erf(\frac{x}{\sqrt{2}}))
  
      Parameters:
          x (Tensor): The input Tensor with data type float32, float64.
          approximate (bool, optional): Wether to enable approximation. Default is False.
          name (str, optional): Name for the operation (optional, default is None).
              For more information, please refer to :ref:`api_guide_Name`.
  
      Returns:
          A Tensor with the same data type and shape as ``x`` .
  
      Examples:
          .. code-block:: python
  
              import paddle
              import paddle.nn.functional as F
  
              x = paddle.to_tensor([[-1, 0.5], [1, 1.5]])
              out1 = F.gelu(x)
              # [[-0.15865529,  0.34573123],
              #  [ 0.84134471,  1.39978933]]
              out2 = F.gelu(x, True)
              # [[-0.15880799,  0.34571400],
              #  [ 0.84119201,  1.39957154]]
      """
  
      if in_dynamic_mode():
          return _C_ops.gelu(x, 'approximate', approximate)
  
      check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'gelu')
      helper = LayerHelper("gelu", **locals())
      out = helper.create_variable_for_type_inference(x.dtype)
      helper.append_op(
          type='gelu',
          inputs={'X': x},
          outputs={'Out': out},
          attrs={'approximate': approximate})
      return out
  
  
  def hardshrink(x, threshold=0.5, name=None):
      r"""
      hard shrinkage activation
  
      .. math::
  
          hardshrink(x)=
              \left\{
                  \begin{array}{rcl}
                  x,&  &if \ {x > threshold}  \\
                  x,&  &if \ {x < -threshold}   \\
                  0,&  &if \ {others} &
                  \end{array}
              \right.
  
      Args:
          x (Tensor): The input Tensor with data type float32, float64.
          threshold (float, optional): The value of threshold for hardthrink. Default is 0.5
          name (str, optional): Name for the operation (optional, default is None).
              For more information, please refer to :ref:`api_guide_Name`.
  
      Returns:
          A Tensor with the same data type and shape as ``x`` .
  
      Examples:
          .. code-block:: python
  
              import paddle
              import paddle.nn.functional as F
  
              x = paddle.to_tensor([-1, 0.3, 2.5])
              out = F.hardshrink(x) # [-1., 0., 2.5]
  
      """
      if in_dynamic_mode():
          return _C_ops.hard_shrink(x, 'threshold', threshold)
  
      check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'],
                               'hardshrink')
      helper = LayerHelper('hardshrink', **locals())
      out = helper.create_variable_for_type_inference(x.dtype)
      helper.append_op(
          type='hard_shrink',
          inputs={'X': x},
          outputs={'Out': out},
          attrs={'threshold': threshold})
      return out
  
  
  def hardtanh(x, min=-1.0, max=1.0, name=None):
      r"""
      hardtanh activation
  
      .. math::
  
          hardtanh(x)=
              \left\{
                  \begin{array}{cll}
                      max,& & \text{if } x > max \\
                      min,& & \text{if } x < min \\
                      x,& & \text{otherwise}
                  \end{array}
              \right.
  
      Parameters:
          x (Tensor): The input Tensor with data type float32, float64.
          min (float, optional): The minimum value of the linear region range. Default is -1.
          max (float, optional): The maximum value of the linear region range. Default is 1.
          name (str, optional): Name for the operation (optional, default is None).
              For more information, please refer to :ref:`api_guide_Name`.
  
      Returns:
          A Tensor with the same data type and shape as ``x`` .
  
      Examples:
          .. code-block:: python
  
              import paddle
              import paddle.nn.functional as F
              import numpy as np
  
              x = paddle.to_tensor(np.array([-1.5, 0.3, 2.5]))
              out = F.hardtanh(x) # [-1., 0.3, 1.]
      """
  
      if in_dynamic_mode():
          return _C_ops.brelu(x, 't_min', min, 't_max', max)
  
      check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'],
                               'hardtanh')
  
      helper = LayerHelper('hardtanh', **locals())
      out = helper.create_variable_for_type_inference(dtype=x.dtype)
      helper.append_op(
          type='brelu',
          inputs={'X': x},
          outputs={'Out': out},
          attrs={'t_min': min,
                 't_max': max})
      return out
  
  
  def hardsigmoid(x, slope=0.1666667, offset=0.5, name=None):
      r"""
      hardsigmoid activation.
  
      A 3-part piecewise linear approximation of sigmoid(https://arxiv.org/abs/1603.00391),
      which is much faster than sigmoid.
  
      .. math::
  
          hardsigmoid(x)=
              \left\{
                  \begin{array}{lcl}
                  0, & &\text{if } \ x \leq -3 \\
                  1, & &\text{if } \ x \geq 3 \\
                  slope * x + offset, & &\text{otherwise}
                  \end{array}
              \right.
  
      Parameters:
          x (Tensor): The input Tensor with data type float32, float64.
          slope (float, optional): The slope of hardsigmoid function. Default is 0.1666667.
          offset (float, optional): The offset of hardsigmoid function. Default is 0.5.
          name (str, optional): Name for the operation (optional, default is None).
              For more information, please refer to :ref:`api_guide_Name`.
  
      Returns:
          A Tensor with the same data type and shape as ``x`` .
  
      Examples:
          .. code-block:: python
  
              import paddle
              import paddle.nn.functional as F
  
              x = paddle.to_tensor([-4., 5., 1.])
              out = F.hardsigmoid(x) # [0., 1., 0.666667]
      """
  
      if in_dynamic_mode():
          return _C_ops.hard_sigmoid(x, 'slope', slope, 'offset', offset)
  
      check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'],
                               'hardsigmoid')
  
      helper = LayerHelper('hardsigmoid', **locals())
      out = helper.create_variable_for_type_inference(x.dtype)
      helper.append_op(
          type='hard_sigmoid',
          inputs={'X': x},
          outputs={'Out': out},
          attrs={'slope': slope,
                 'offset': offset})
      return out
  
  
  def hardswish(x, name=None):
      r"""
      hardswish activation
  
      hardswish is proposed in MobileNetV3, and performs better in computational stability
      and efficiency compared to swish function. For more details please refer
      to: https://arxiv.org/pdf/1905.02244.pdf
  
      .. math::
  
          hardswish(x)=
              \left\{
                  \begin{array}{cll}
                  0 &, & \text{if } x \leq -3 \\
                  x &, & \text{if } x \geq 3 \\
                  \frac{x(x+3)}{6} &, & \text{otherwise}
                  \end{array}
              \right.
  
      Parameters:
          x (Tensor): The input Tensor with data type float32, float64.
          name (str, optional): Name for the operation (optional, default is None).
              For more information, please refer to :ref:`api_guide_Name`.
  
      Returns:
          A Tensor with the same data type and shape as ``x`` .
  
      Examples:
          .. code-block:: python
  
              import paddle
              import paddle.nn.functional as F
  
              x = paddle.to_tensor([-4., 5., 1.])
              out = F.hardswish(x) # [0., 5., 0.666667]
      """
  
      if in_dynamic_mode():
          return _C_ops.hard_swish(x)
  
      check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'],
                               'hardswish')
  
      helper = LayerHelper('hardswish', **locals())
      out = helper.create_variable_for_type_inference(x.dtype)
      helper.append_op(type='hard_swish', inputs={'X': x}, outputs={'Out': out})
      return out
  
  
  def leaky_relu(x, negative_slope=0.01, name=None):
      r"""
      leaky_relu activation
  
      .. math::
          leaky\_relu(x)=
          \left\{
              \begin{array}{rcl}
                  x, & & if \ x >= 0 \\
                  negative\_slope * x, & & otherwise \\
              \end{array}
          \right.
  
      Args:
          x (Tensor): The input Tensor with data type float32, float64.
          negative_slope (float, optional): Slope of the activation function at
              :math:`x < 0` . Default is 0.01.
          name (str, optional): Name for the operation (optional, default is None).
              For more information, please refer to :ref:`api_guide_Name`.
  
      Returns:
          A Tensor with the same data type and shape as ``x`` .
  
      Examples:
          .. code-block:: python
  
              import paddle
              import paddle.nn.functional as F
  
              x = paddle.to_tensor([-2., 0., 1.])
              out = F.leaky_relu(x) # [-0.02, 0., 1.]
  
      """
      if in_dynamic_mode():
          return _C_ops.leaky_relu(x, 'alpha', negative_slope)
  
      check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'],
                               'leaky_relu')
      helper = LayerHelper('leaky_relu', **locals())
      out = helper.create_variable_for_type_inference(dtype=x.dtype)
      helper.append_op(
          type='leaky_relu',
          inputs={'X': x},
          outputs={'Out': out},
          attrs={'alpha': negative_slope})
      return out
  
  
  def prelu(x, weight, data_format="NCHW", name=None):
      """
      prelu activation.
  
      .. math::
  
          prelu(x) = max(0, x) + weight * min(0, x)
  
      Parameters:
          x (Tensor): The input Tensor with data type float32, float64.
          weight (Tensor): The learnable parameter with data type same as ``x``.
              The weight shape is [1] or [in], where `in` is the input channel of ``x``.
          name (str, optional): Name for the operation (optional, default is None).
              For more information, please refer to :ref:`api_guide_Name`.
          data_format(str, optional): Data format that specifies the layout of input.
              It may be "NC", "NCL", "NCHW", "NCDHW", "NLC", "NHWC" or "NDHWC". Default: "NCHW".
  
      Returns:
          A Tensor with the same data type and shape as ``x`` .
  
      Examples:
          .. code-block:: python
  
              import paddle
              import paddle.nn.functional as F
              import numpy as np
  
              data = np.array([[[[-2.0,  3.0, -4.0,  5.0],
                                 [ 3.0, -4.0,  5.0, -6.0],
                                 [-7.0, -8.0,  8.0,  9.0]],
                                [[ 1.0, -2.0, -3.0,  4.0],
                                 [-5.0,  6.0,  7.0, -8.0],
                                 [ 6.0,  7.0,  8.0,  9.0]]]], 'float32')
              x = paddle.to_tensor(data)
              w = paddle.to_tensor(np.array([0.25]).astype('float32'))
              out = F.prelu(x, w)
              # [[[[-0.5 ,  3.  , -1.  ,  5.  ],
              #    [ 3.  , -1.  ,  5.  , -1.5 ],
              #    [-1.75, -2.  ,  8.  ,  9.  ]],
              #   [[ 1.  , -0.5 , -0.75,  4.  ],
              #    [-1.25,  6.  ,  7.  , -2.  ],
              #    [ 6.  ,  7.  ,  8.  ,  9.  ]]]]
      """
      check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'prelu')
      check_variable_and_dtype(weight, 'weight',
                               ['float16', 'float32', 'float64'], 'prelu')
  
      assert len(weight.shape
                 ) == 1, "The dim count of weight shape should be 1 in prelu()."
  
      mode = 'all'
      if weight.shape[0] > 1:
  
          true_data_format = [
              'NC', 'NCL', 'NCHW', 'NCDHW', 'NLC', 'NHWC', 'NDHWC'
          ]
          if data_format not in true_data_format:
              raise ValueError(
                  "data_format must be one of 'NC', 'NCL', 'NCHW', 'NCDHW', "
                  "'NLC', 'NHWC', 'NDHWC' but receive {}".format(data_format))
  
          data_format = 'NCHW' if data_format[1] == 'C' else 'NHWC'
  
          assert len(
              x.shape
          ) > 1, "The dim count of x should be equal or larger than 2 in prelu() when weight shape is not [1]."
  
          #NOTE(GuoxiaWang): support NHWC data format
          if data_format == 'NHWC':
              assert weight.shape[0] == x.shape[
                  -1], "The weight size should be equal to x input channel in prelu() when weight shape is not [1]."
          else:
              assert weight.shape[0] == x.shape[
                  1], "The weight size should be equal to x input channel in prelu() when weight shape is not [1]."
          mode = 'channel'
  
      if in_dygraph_mode():
          return _C_ops.final_state_prelu(x, weight, data_format, mode)
      if _in_legacy_dygraph():
          return _C_ops.prelu(x, weight, 'mode', mode, 'data_format', data_format)
  
      helper = LayerHelper('prelu', **locals())
      out = helper.create_variable_for_type_inference(x.dtype)
      helper.append_op(
          type="prelu",
          inputs={"X": x,
                  "Alpha": weight},
          outputs={"Out": out},
          attrs={"mode": mode,
                 "data_format": data_format})
      return out
  
  
  def relu(x, name=None):
      """
      relu activation.
  
      .. math::
  
          out = max(x, 0)
  
      Parameters:
          x (Tensor): The input Tensor with data type float32, float64.
          name (str, optional): Name for the operation (optional, default is None).
              For more information, please refer to :ref:`api_guide_Name`.
  
      Returns:
          A Tensor with the same data type and shape as ``x`` .
  
      Examples:
          .. code-block:: python
  
              import paddle
              import paddle.nn.functional as F
              import numpy as np
  
              x = paddle.to_tensor(np.array([-2, 0, 1]).astype('float32'))
              out = F.relu(x) # [0., 0., 1.]
      """
  
      if in_dygraph_mode():
          return _C_ops.final_state_relu(x)
      if _in_legacy_dygraph():
          return _C_ops.relu(x)
      check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'relu')
      helper = LayerHelper('relu', **locals())
      out = helper.create_variable_for_type_inference(x.dtype)
      helper.append_op(type='relu', inputs={'X': x}, outputs={'Out': out})
      return out
  
  
  @inplace_apis_in_dygraph_only
  def relu_(x, name=None):
      """
      Inplace version of ``relu`` API, the output Tensor will be inplaced with input ``x``.
      Please refer to :ref:`api_nn_cn_relu`.
      """
      if in_dygraph_mode():
          return _C_ops.final_state_relu_(x)
      if _in_legacy_dygraph():
          return _C_ops.relu_(x)
  
  
  def log_sigmoid(x, name=None):
      r"""
      log_sigmoid activation.
  
      .. math::
  
          log\_sigmoid(x) = log \frac{1}{1 + e^{-x}}
  
      Parameters:
          x (Tensor): The input Tensor with data type float32, float64.
          name (str, optional): Name for the operation (optional, default is None).
              For more information, please refer to :ref:`api_guide_Name`.
  
      Returns:
          A Tensor with the same data type and shape as ``x`` .
  
      Examples:
          .. code-block:: python
  
              import paddle
              import paddle.nn.functional as F
  
              x = paddle.to_tensor([1.0, 2.0, 3.0, 4.0])
              out = F.log_sigmoid(x) # [-0.313262 -0.126928 -0.0485874 -0.0181499]
      """
  
      if in_dynamic_mode():
          return _C_ops.logsigmoid(x)
  
      check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'],
                               'log_sigmoid')
      helper = LayerHelper("log_sigmoid", **locals())
      out = helper.create_variable_for_type_inference(x.dtype)
      helper.append_op(type='logsigmoid', inputs={'X': x}, outputs={'Out': out})
      return out
  
  
  def maxout(x, groups, axis=1, name=None):
      r"""
      maxout activation.
  
      Assumed the input shape is (N, Ci, H, W).
      The output shape is (N, Co, H, W).
      Then Co = Ci/groups and the operator formula is as follows:
  
      .. math::
  
          \begin{array}{l}
          &out_{si+j} = \max_{k} x_{gsi + sk + j} \\
          &g = groups \\
          &s = \frac{input.size}{num\_channels} \\
          &0 \le i < \frac{num\_channels}{groups} \\
          &0 \le j < s \\
          &0 \le k < groups
          \end{array}
  
  
      Parameters:
          x (Tensor): The input is 4-D Tensor with shape [N, C, H, W] or [N, H, W, C], the data type
              of input is float32 or float64.
          groups (int, optional): The groups number of maxout. `groups` specifies the
              index of channel dimension where maxout will be performed. This must be
              a factor of number of features. Default is 1.
          axis (int, optional): The axis along which to perform maxout calculations.
              It should be 1 when data format is NCHW, be -1 or 3 when data format
              is NHWC. If ``axis`` < 0, it works the same way as :math:`axis + D` ,
              where D is the dimensions of ``x`` . ``axis`` only supports 1, 3 or -1.
              Default is 1.
          name (str, optional): Name for the operation (optional, default is None).
              For more information, please refer to :ref:`api_guide_Name`.
  
      Returns:
          A Tensor with the same data type as ``x`` .
  
      Examples:
          .. code-block:: python
  
              import paddle
              import paddle.nn.functional as F
  
              x = paddle.rand([1, 2, 3, 4])
              # [[[[0.5002636  0.22272532 0.17402348 0.2874594 ]
              #    [0.95313174 0.6228939  0.7129065  0.7087491 ]
              #    [0.02879342 0.88725346 0.61093384 0.38833922]]
              #   [[0.5231306  0.03807496 0.91661984 0.15602879]
              #    [0.666127   0.616567   0.30741522 0.24044901]
              #    [0.7142536  0.7351477  0.31588817 0.23782359]]]]
              out = F.maxout(x, groups=2)
              # [[[[0.5231306  0.22272532 0.91661984 0.2874594 ]
              #    [0.95313174 0.6228939  0.7129065  0.7087491 ]
              #    [0.7142536  0.88725346 0.61093384 0.38833922]]]]
      """
  
      if in_dynamic_mode():
          return _C_ops.maxout(x, 'groups', groups, 'axis', axis)
  
      check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'maxout')
      if axis not in [1, -1, 3]:
          raise ValueError(
              "Attr(axis) should be 1 when data format is NCHW, -1 or 3 when data format is NHWC. Received "
              "Attr(axis): %s." % str(axis))
      if axis == -1:
          axis = 3
  
      helper = LayerHelper('maxout', **locals())
      out = helper.create_variable_for_type_inference(x.dtype)
      helper.append_op(
          type='maxout',
          inputs={'X': x},
          outputs={'Out': out},
          attrs={'groups': groups,
                 'axis': axis})
      return out
  
  
  def relu6(x, name=None):
      """
      relu6 activation
  
      .. math::
  
          relu6(x) = min(max(0,x), 6)
  
      Parameters:
          x (Tensor): The input Tensor with data type float32, float64.
          name (str, optional): Name for the operation (optional, default is None).
              For more information, please refer to :ref:`api_guide_Name`.
  
      Returns:
          A Tensor with the same data type and shape as ``x`` .
  
      Examples:
          .. code-block:: python
  
              import paddle
              import paddle.nn.functional as F
              import numpy as np
  
              x = paddle.to_tensor(np.array([-1, 0.3, 6.5]))
              out = F.relu6(x) # [0, 0.3, 6]
      """
      threshold = 6.0
      if in_dynamic_mode():
          return _C_ops.relu6(x, 'threshold', threshold)
  
      check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'relu6')
      helper = LayerHelper('relu6', **locals())
      out = helper.create_variable_for_type_inference(x.dtype)
      helper.append_op(
          type='relu6',
          inputs={'X': x},
          outputs={'Out': out},
          attrs={'threshold': threshold})
      return out
  
  
  def selu(x,
           scale=1.0507009873554804934193349852946,
           alpha=1.6732632423543772848170429916717,
           name=None):
      r"""
      selu activation
  
      .. math::
  
          selu(x)= scale *
              \left\{
                  \begin{array}{lcl}
                  x,& &\text{if } \ x > 0 \\
                  alpha * e^{x} - alpha,& &\text{if } \ x <= 0
                  \end{array}
              \right.
  
      Parameters:
          x (Tensor): The input Tensor with data type float32, float64.
          scale (float, optional): The value of scale(must be greater than 1.0) for selu. Default is 1.0507009873554804934193349852946
          alpha (float, optional): The value of alpha(must be no less than zero) for selu. Default is 1.6732632423543772848170429916717
          name (str, optional): Name for the operation (optional, default is None).
              For more information, please refer to :ref:`api_guide_Name`.
  
      Returns:
          A Tensor with the same data type and shape as ``x`` .
  
      Examples:
          .. code-block:: python
  
              import paddle
              import paddle.nn.functional as F
              import numpy as np
  
              x = paddle.to_tensor(np.array([[0.0, 1.0],[2.0, 3.0]]))
              out = F.selu(x) # [[0, 1.050701],[2.101402, 3.152103]]
      """
      if scale <= 1.0:
          raise ValueError(
              "The scale must be greater than 1.0. Received: {}.".format(scale))
  
      if alpha < 0:
          raise ValueError(
              "The alpha must be no less than zero. Received: {}.".format(alpha))
  
      if in_dygraph_mode():
          return _C_ops.final_state_selu(x, scale, alpha)
      if _in_legacy_dygraph():
          return _C_ops.selu(x, 'scale', scale, 'alpha', alpha)
  
      check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'selu')
      helper = LayerHelper('selu', **locals())
      out = helper.create_variable_for_type_inference(x.dtype)
      helper.append_op(
          type='selu',
          inputs={'X': x},
          outputs={'Out': out},
          attrs={'scale': scale,
                 'alpha': alpha})
      return out
  
  
  def silu(x, name=None):
      r"""
      silu activation
  
      .. math::
  
          silu(x) = \frac{x}{1 + e^{-x}}
      
      Parameters:
          x (Tensor): The input Tensor with data type float32, float64.
          name (str, optional): Name for the operation (optional, default is None).
              For more information, please refer to :ref:`api_guide_Name`.
      
      Returns:
          A Tensor with the same data type and shape as ``x`` .
      
      Examples:
          .. code-block:: python
  
              import paddle
              import paddle.nn.functional as F
              
              x = paddle.to_tensor([1.0, 2.0, 3.0, 4.0])
              out = F.silu(x) # [ 0.731059, 1.761594, 2.857722, 3.928055 ]
      """
  
      if in_dynamic_mode():
          return _C_ops.silu(x)
  
      check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'silu')
      helper = LayerHelper("silu", **locals())
      out = helper.create_variable_for_type_inference(x.dtype)
      helper.append_op(type='silu', inputs={'X': x}, outputs={'Out': out})
      return out
  
  
  def softmax(x, axis=-1, dtype=None, name=None):
      r"""
      This operator implements the softmax layer. The calculation process is as follows:
  
      1. The dimension :attr:`axis` of ``x`` will be permuted to the last.
  
      2. Then ``x`` will be logically flattened to a 2-D matrix. The matrix's second
      dimension(row length) is the same as the dimension :attr:`axis` of ``x``,
      and the first dimension(column length) is the product of all other dimensions
      of ``x``. For each row of the matrix, the softmax operator squashes the
      K-dimensional(K is the width of the matrix, which is also the size of ``x``'s
      dimension :attr:`axis`) vector of arbitrary real values to a K-dimensional
      vector of real values in the range [0, 1] that add up to 1.
  
      3. After the softmax operation is completed, the inverse operations of steps 1 and 2
      are performed to restore the two-dimensional matrix to the same dimension as the ``x`` .
  
      It computes the exponential of the given dimension and the sum of exponential
      values of all the other dimensions in the K-dimensional vector input.
      Then the ratio of the exponential of the given dimension and the sum of
      exponential values of all the other dimensions is the output of the softmax
      operator.
  
      For each row :math:`i` and each column :math:`j` in the matrix, we have:
  
      .. math::
  
          softmax[i, j] = \frac{\exp(x[i, j])}{\sum_j(exp(x[i, j])}
  
      Example:
  
      .. code-block:: text
  
          Case 1:
            Input:
              x.shape = [2, 3, 4]
              x.data = [[[2.0, 3.0, 4.0, 5.0],
                         [3.0, 4.0, 5.0, 6.0],
                         [7.0, 8.0, 8.0, 9.0]],
                        [[1.0, 2.0, 3.0, 4.0],
                         [5.0, 6.0, 7.0, 8.0],
                         [6.0, 7.0, 8.0, 9.0]]]
  
            Attrs:
              axis = -1
  
            Output:
              out.shape = [2, 3, 4]
              out.data = [[[0.0320586 , 0.08714432, 0.23688282, 0.64391426],
                           [0.0320586 , 0.08714432, 0.23688282, 0.64391426],
                           [0.07232949, 0.19661193, 0.19661193, 0.53444665]],
                          [[0.0320586 , 0.08714432, 0.23688282, 0.64391426],
                           [0.0320586 , 0.08714432, 0.23688282, 0.64391426],
                           [0.0320586 , 0.08714432, 0.23688282, 0.64391426]]]
  
          Case 2:
            Input:
              x.shape = [2, 3, 4]
              x.data = [[[2.0, 3.0, 4.0, 5.0],
                         [3.0, 4.0, 5.0, 6.0],
                         [7.0, 8.0, 8.0, 9.0]],
                        [[1.0, 2.0, 3.0, 4.0],
                         [5.0, 6.0, 7.0, 8.0],
                         [6.0, 7.0, 8.0, 9.0]]]
            Attrs:
              axis = 1
  
            Output:
              out.shape = [2, 3, 4]
              out.data = [[[0.00657326, 0.00657326, 0.01714783, 0.01714783],
                           [0.01786798, 0.01786798, 0.04661262, 0.04661262],
                           [0.97555875, 0.97555875, 0.93623955, 0.93623955]],
                          [[0.00490169, 0.00490169, 0.00490169, 0.00490169],
                           [0.26762315, 0.26762315, 0.26762315, 0.26762315],
                           [0.72747516, 0.72747516, 0.72747516, 0.72747516]]]
  
      Parameters:
          x (Tensor): The input Tensor with data type float32, float64.
          axis (int, optional): The axis along which to perform log_softmax
              calculations. It should be in range [-D, D), where D is the
              dimensions of ``x`` . If ``axis`` < 0, it works the same way as
              :math:`axis + D` . Default is -1.
          dtype (str, optional): The data type of the output tensor, can be float32, float64.
          name (str, optional): Name for the operation (optional, default is None).
              For more information, please refer to :ref:`api_guide_Name`.
  
      Returns:
          A Tensor with the same shape and data type (use ``dtype`` if it is
          specified) as x.
  
      Examples:
          .. code-block:: python
  
              import paddle
              import paddle.nn.functional as F
              import numpy as np
  
              x = np.array([[[2.0, 3.0, 4.0, 5.0],
                          [3.0, 4.0, 5.0, 6.0],
                          [7.0, 8.0, 8.0, 9.0]],
                          [[1.0, 2.0, 3.0, 4.0],
                          [5.0, 6.0, 7.0, 8.0],
                          [6.0, 7.0, 8.0, 9.0]]], 'float32')
              x = paddle.to_tensor(x)
              out1 = F.softmax(x)
              out2 = F.softmax(x, dtype='float64')
              # out1's data type is float32; out2's data type is float64
              # out1 and out2's value is as follows:
              # [[[0.0320586 , 0.08714432, 0.23688282, 0.64391426],
              #   [0.0320586 , 0.08714432, 0.23688282, 0.64391426],
              #   [0.07232949, 0.19661193, 0.19661193, 0.53444665]],
              # [[0.0320586 , 0.08714432, 0.23688282, 0.64391426],
              #   [0.0320586 , 0.08714432, 0.23688282, 0.64391426],
              #   [0.0320586 , 0.08714432, 0.23688282, 0.64391426]]]
      """
  
      if (dtype is not None) and (not isinstance(dtype, core.VarDesc.VarType)):
          dtype = convert_np_dtype_to_dtype_(dtype)
      use_cudnn = True
  
      if in_dygraph_mode():
          outs_cast = x if dtype is None \
              else _C_ops.cast(x, 'in_dtype', x.dtype, 'out_dtype', dtype)
          return _C_ops.final_state_softmax(outs_cast, axis)
  
      if _in_legacy_dygraph():
          outs_cast = x if dtype is None \
              else _C_ops.cast(x, 'in_dtype', x.dtype, 'out_dtype', dtype)
          return _C_ops.softmax(outs_cast, 'axis', axis, 'use_cudnn', use_cudnn)
  
      if dtype is None:
          check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'],
                                   'softmax')
      else:
          check_dtype(dtype, 'dtype', ['float32', 'float64'], 'softmax',
                      'If dtype is not None, it only support float32 or float64.')
  
      helper = LayerHelper("softmax", **locals())
      outs_cast = x
      if dtype is not None:
          outs_cast = helper.create_variable_for_type_inference(dtype)
          helper.append_op(
              type='cast',
              inputs={'X': x},
              outputs={'Out': outs_cast},
              attrs={'in_dtype': x.dtype,
                     'out_dtype': dtype})
  
      outs_softmax = helper.create_variable_for_type_inference(outs_cast.dtype)
      helper.append_op(
          type='softmax',
          inputs={'X': outs_cast},
          outputs={'Out': outs_softmax},
          attrs={'axis': axis,
                 'use_cudnn': use_cudnn})
  
      return outs_softmax
  
  
  @inplace_apis_in_dygraph_only
  def softmax_(x, axis=-1, dtype=None, name=None):
      r"""
      Inplace version of ``softmax`` API, the output Tensor will be inplaced with input ``x``.
      Please refer to :ref:`api_nn_cn_softmax`.
      """
      if (dtype is not None) and (not isinstance(dtype, core.VarDesc.VarType)):
          dtype = convert_np_dtype_to_dtype_(dtype)
      use_cudnn = True
      return _C_ops.softmax_(x, 'axis', axis, 'use_cudnn', use_cudnn)
  
  
  def softplus(x, beta=1, threshold=20, name=None):
      r"""
      softplus activation
  
      .. math::
  
          softplus(x) = \frac{1}{beta} * \log(1 + e^{beta * x}) \\
          \text{For numerical stability, the implementation reverts to the linear function when: beta * x > threshold.}
  
      Parameters:
          x (Tensor): The input Tensor with data type float32, float64.
          beta (float, optional): The value of beta for softplus. Default is 1
          threshold (float, optional): The value of threshold for softplus. Default is 20
          name (str, optional): Name for the operation (optional, default is None).
              For more information, please refer to :ref:`api_guide_Name`.
  
      Returns:
          A Tensor with the same data type and shape as ``x`` .
  
      Examples:
          .. code-block:: python
  
              import paddle
              import paddle.nn.functional as F
              import numpy as np
  
              x = paddle.to_tensor(np.array([-0.4, -0.2, 0.1, 0.3]))
              out = F.softplus(x) # [0.513015, 0.598139, 0.744397, 0.854355]
      """
      if in_dynamic_mode():
          return _C_ops.softplus(x, 'beta', beta, 'threshold', threshold)
  
      check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'],
                               'softplus')
      helper = LayerHelper('softplus', **locals())
      out = helper.create_variable_for_type_inference(x.dtype)
      helper.append_op(
          type='softplus',
          inputs={'X': x},
          outputs={'Out': out},
          attrs={'beta': beta,
                 'threshold': threshold})
      return out
  
  
  def softshrink(x, threshold=0.5, name=None):
      r"""
      softshrink activation
  
      .. math::
  
          softshrink(x)= 
              \left\{
                  \begin{array}{rcl}
                  x - threshold,& & \text{if } x > threshold \\
                  x + threshold,& & \text{if } x < -threshold \\
                  0,& &  \text{otherwise}
              \end{array}
              \right.
  
      Parameters:
          x (Tensor): The input Tensor with data type float32, float64.
          threshold (float, optional): The value of threshold(must be no less than zero) for softplus. Default is 0.5
          name (str, optional): Name for the operation (optional, default is None).
              For more information, please refer to :ref:`api_guide_Name`.
  
      Returns:
          A Tensor with the same data type and shape as ``x`` .
  
      Examples:
          .. code-block:: python
  
              import paddle
              import paddle.nn.functional as F
              import numpy as np
  
              x = paddle.to_tensor(np.array([-0.9, -0.2, 0.1, 0.8]))
              out = F.softshrink(x) # [-0.4, 0, 0, 0.3]
      """
      if threshold < 0:
          raise ValueError(
              "The threshold must be no less than zero. Received: {}.".format(
                  threshold))
  
      if in_dygraph_mode():
          return _C_ops.final_state_soft_shrink(x, threshold)
      if _in_legacy_dygraph():
          return _C_ops.softshrink(x, 'lambda', threshold)
  
      check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'],
                               'softshrink')
      helper = LayerHelper('softshrink', **locals())
      out = helper.create_variable_for_type_inference(x.dtype)
      helper.append_op(
          type='softshrink',
          inputs={'X': x},
          outputs={'Out': out},
          attrs={'lambda': threshold})
      return out
  
  
  def softsign(x, name=None):
      r"""
      softsign activation
  
      .. math::
  
          softsign(x) = \frac{x}{1 + |x|}
  
      Parameters:
          x (Tensor): The input Tensor with data type float32, float64.
          name (str, optional): Name for the operation (optional, default is None).
              For more information, please refer to :ref:`api_guide_Name`.
  
      Returns:
          A Tensor with the same data type and shape as ``x`` .
  
      Examples:
          .. code-block:: python
  
              import paddle
              import paddle.nn.functional as F
              import numpy as np
  
              x = paddle.to_tensor(np.array([-0.4, -0.2, 0.1, 0.3]))
              out = F.softsign(x) # [-0.285714, -0.166667, 0.0909091, 0.230769]
      """
      if in_dynamic_mode():
          return _C_ops.softsign(x)
  
      check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'],
                               'softsign')
      helper = LayerHelper('softsign', **locals())
      out = helper.create_variable_for_type_inference(x.dtype)
      helper.append_op(type='softsign', inputs={'X': x}, outputs={'Out': out})
      return out
  
  
  def swish(x, name=None):
      r"""
      swish activation.
  
      .. math::
  
          swish(x) = \frac{x}{1 + e^{-x}}
  
      Parameters:
          x (Tensor): The input Tensor with data type float32, float64.
          name (str, optional): Name for the operation (optional, default is None).
              For more information, please refer to :ref:`api_guide_Name`.
  
      Returns:
          A Tensor with the same data type and shape as ``x`` .
  
      Examples:
          .. code-block:: python
  
              import paddle
              import paddle.nn.functional as F
              import numpy as np
  
              x = paddle.to_tensor(np.array([-2., 0., 1.]))
              out = F.swish(x) # [-0.238406, 0., 0.731059]
      """
  
      if in_dynamic_mode():
          return _C_ops.swish(x, 'beta', 1.0)
  
      check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'swish')
      helper = LayerHelper('swish', **locals())
      out = helper.create_variable_for_type_inference(x.dtype)
      helper.append_op(
          type='swish',
          inputs={'X': x},
          outputs={'Out': out},
          attrs={'beta': 1.0})
      return out
  
  
  def mish(x, name=None):
      r"""
      mish activation.
  
      ..  math::
  
          softplus(x) = \begin{cases}
                  x, \text{if } x > \text{threshold} \\
                  \ln(1 + e^{x}),  \text{otherwise}
              \end{cases}
  
          mish(x) = x * \tanh(softplus(x))
      
      Parameters:
          x (Tensor): The input Tensor with data type float32, float64.
          name (str, optional): Name for the operation (optional, default is None).
              For more information, please refer to :ref:`api_guide_Name`.
  
      Returns:
          A Tensor with the same data type and shape as ``x`` .
  
      Examples:
          .. code-block:: python
  
              import paddle
              import paddle.nn.functional as F
  
              x = paddle.to_tensor([-5., 0., 5.])
              out = F.mish(x) # [-0.03357624, 0., 4.99955208]
      """
      if in_dygraph_mode():
          return _C_ops.final_state_mish(x, 20)
      if _in_legacy_dygraph():
          return _C_ops.mish(x)
  
      check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'mish')
      helper = LayerHelper('mish', **locals())
      out = helper.create_variable_for_type_inference(x.dtype)
      helper.append_op(type='mish', inputs={'X': x}, outputs={'Out': out})
      return out
  
  
  def tanhshrink(x, name=None):
      """
      tanhshrink activation
  
      .. math::
  
          tanhshrink(x) = x - tanh(x)
  
      Args:
          x (Tensor): The input Tensor with data type float32, float64.
          name (str, optional): Name for the operation (optional, default is None).
              For more information, please refer to :ref:`api_guide_Name`.
  
      Returns:
          A Tensor with the same data type and shape as ``x`` .
  
      Examples:
          .. code-block:: python
  
              import paddle
              import paddle.nn.functional as F
              import numpy as np
  
              x = paddle.to_tensor(np.array([-0.4, -0.2, 0.1, 0.3]))
              out = F.tanhshrink(x) # [-0.020051, -0.00262468, 0.000332005, 0.00868739]
      """
      if in_dynamic_mode():
          return _C_ops.tanh_shrink(x)
  
      check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'],
                               'tanhshrink')
      helper = LayerHelper('tanh_shrink', **locals())
      out = helper.create_variable_for_type_inference(x.dtype)
      helper.append_op(type='tanh_shrink', inputs={'X': x}, outputs={'Out': out})
      return out
  
  
  def thresholded_relu(x, threshold=1.0, name=None):
      r"""
      thresholded relu activation.
  
      .. math::
  
          thresholded\_relu(x) = 
              \left\{
                  \begin{array}{rl}
                  x,& \text{if } \ x > threshold \\
                  0,& \text{otherwise}
                  \end{array}
              \right.
  
  
      Parameters:
          x (Tensor): The input Tensor with data type float32, float64.
          threshold (float, optional): The value of threshold for thresholded_relu. Default is 1.0
          name (str, optional): Name for the operation (optional, default is None).
              For more information, please refer to :ref:`api_guide_Name`.
  
      Returns:
          A Tensor with the same data type and shape as ``x`` .
  
      Examples:
          .. code-block:: python
  
              import paddle
              import paddle.nn.functional as F
              import numpy as np
  
              x = paddle.to_tensor(np.array([2., 0., 1.]))
              out = F.thresholded_relu(x) # [2., 0., 0.]
      """
  
      if in_dynamic_mode():
          return _C_ops.thresholded_relu(x, 'threshold', threshold)
  
      check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'],
                               'thresholded_relu')
      helper = LayerHelper('thresholded_relu', **locals())
      out = helper.create_variable_for_type_inference(x.dtype)
      helper.append_op(
          type='thresholded_relu',
          inputs={'X': x},
          outputs={'Out': out},
          attrs={'threshold': threshold})
      return out
  
  
  def log_softmax(x, axis=-1, dtype=None, name=None):
      r"""
      This operator implements the log_softmax layer. The calculation process is
      as follows:
  
      .. math::
  
          \begin{aligned} 
          log\_softmax[i, j] &= log(softmax(x)) \\
          &= log(\frac{\exp(X[i, j])}{\sum_j(\exp(X[i, j])})
          \end{aligned}
  
      Parameters:
          x (Tensor): The input Tensor with data type float32, float64.
          axis (int, optional): The axis along which to perform log_softmax
              calculations. It should be in range [-D, D), where D is the
              dimensions of ``x`` . If ``axis`` < 0, it works the same way as
              :math:`axis + D` . Default is -1.
          dtype (str|np.dtype|core.VarDesc.VarType, optional): The desired data
              type of the output tensor. If dtype is specified, ``x`` is casted
              to ``dtype`` before the operation is performed. This is useful for
              preventing data type overflows. Supported dtype: float32, float64.
              If ``dtype`` is None, the output Tensor has the same dtype as x.
              Default is None.
          name (str, optional): Name for the operation (optional, default is None).
              For more information, please refer to :ref:`api_guide_Name`.
  
      Returns:
          A Tensor with the same shape and data type (use ``dtype`` if it is
          specified) as x.
  
      Examples:
          .. code-block:: python
  
              import paddle
              import paddle.nn.functional as F
  
              x = [[[-2.0, 3.0, -4.0, 5.0],
                    [3.0, -4.0, 5.0, -6.0],
                    [-7.0, -8.0, 8.0, 9.0]],
                   [[1.0, -2.0, -3.0, 4.0],
                    [-5.0, 6.0, 7.0, -8.0],
                    [6.0, 7.0, 8.0, 9.0]]]
              x = paddle.to_tensor(x)
              out1 = F.log_softmax(x)
              out2 = F.log_softmax(x, dtype='float64')
              # out1's data type is float32; out2's data type is float64
              # out1 and out2's value is as follows:
              # [[[ -7.1278396   -2.1278396   -9.127839    -0.12783948]
              #   [ -2.1270514   -9.127051    -0.12705144 -11.127051  ]
              #   [-16.313261   -17.313261    -1.3132617   -0.31326184]]
              #  [[ -3.0518122   -6.051812    -7.051812    -0.051812  ]
              #   [-12.313267    -1.3132664   -0.3132665  -15.313267  ]
              #   [ -3.4401896   -2.4401896   -1.4401896   -0.44018966]]]
      """
  
      if (dtype is not None) and (not isinstance(dtype, core.VarDesc.VarType)):
          dtype = convert_np_dtype_to_dtype_(dtype)
  
      if _non_static_mode():
          if dtype is not None:
              x = _C_ops.cast(x, 'in_dtype', x.dtype, 'out_dtype', dtype)
          if _in_legacy_dygraph():
              return _C_ops.log_softmax(x, 'axis', axis)
          return _C_ops.final_state_log_softmax(x, axis)
  
      if dtype is None:
          check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'],
                                   'log_softmax')
      else:
          check_dtype(dtype, 'dtype', ['float32', 'float64'], 'log_softmax',
                      'If dtype is not None, it only support float32 or float64.')
  
      helper = LayerHelper("log_softmax", **locals())
      out_cast = x
      if dtype is not None:
          out_cast = helper.create_variable_for_type_inference(dtype)
          helper.append_op(
              type='cast',
              inputs={'X': x},
              outputs={'Out': out_cast},
              attrs={'in_dtype': x.dtype,
                     'out_dtype': dtype})
  
      out = helper.create_variable_for_type_inference(out_cast.dtype)
      helper.append_op(
          type='log_softmax',
          inputs={'X': out_cast},
          outputs={'Out': out},
          attrs={'axis': axis})
  
      return out
  
  
  def glu(x, axis=-1, name=None):
      r"""
      The gated linear unit. The input is evenly splited into 2 parts along a 
      given axis. The first part is used as the content, and the second part is
      passed through a sigmoid function then used as the gate. The output is a
      elementwise multiplication of the content and the gate.
  
      .. math::
  
          \mathrm{GLU}(a, b) = a \otimes \sigma(b)
  
      Parameters:
          x (Tensor): The input Tensor with data type float32, float64.
          axis (int, optional): The axis along which split the input tensor. It 
              should be in range [-D, D), where D is the dimensions of ``x`` . 
              If ``axis`` < 0, it works the same way as :math:`axis + D` . 
              Default is -1.
          name (str, optional): Name for the operation (optional, default is None).
              For more information, please refer to :ref:`api_guide_Name`.
      
      Returns:
          A Tensor with the same data type as x. The size of the given aixs is 
          halved.
      
      Examples:
          .. code-block:: python
          
              import paddle
              from paddle.nn import functional as F
              
              x = paddle.to_tensor(
                  [[-0.22014759, -1.76358426,  0.80566144,  0.04241343],
                   [-1.94900405, -1.89956081,  0.17134808, -1.11280477]]
              )
              print(F.glu(x).numpy())
              # array([[-0.15216254, -0.9004892 ],
              #        [-1.0577879 , -0.46985325]], dtype=float32)
          
      """
      check_variable_and_dtype(x, 'input', ['float16', 'float32', 'float64'],
                               "glu")
      a, b = chunk(x, 2, axis=axis, name=name)
      gate = sigmoid(b, name=name)
      out = paddle.multiply(a, gate, name=name)
      return out
  
  
  def gumbel_softmax(x, temperature=1.0, hard=False, axis=-1, name=None):
      r"""
      Samples from the Gumbel-Softmax distribution and optionally discretizes.
      temperature is denoted by t. The calculation process is as follows:
  
      First, generate gumbel noise:
  
      .. math::
  
          G_i = -log(-log(U_i)), U_i \sim U(0,1)
  
      Second, add noise to ``x``:
  
      .. math::
  
          v = [x_1 + G_1,...,x_n + G_n]
  
      Finally, calculate gumbel_softmax and generate samples:
  
      .. math::
          gumbel\_softmax(v_i)=\frac{e^{v_i/t}}{\sum_{j=1}^n{e^{v_j/t}}},i=1,2,3...n
  
      Parameters:
          x (Tensor): An N-D Tensor, the first N - 1 dimensions index into a batch 
              of independent distributions and the last dimension represents 
              a vector of probabilities with datatype float32, float64.
          temperature (float, optional): non-negative scalar temperature.
              Default is 1.0.
          hard (bool, optional): if True, the returned samples will be discretized as 
              one-hot vectors, but will be differentiated as if it is the soft sample 
              in autograd. Default is False.
          axis (int, optional): The axis along will be calculated softmax value. 
              Default is -1.
          name (str, optional): Name for the operation (optional, default is None).
              For more information, please refer to :ref:`api_guide_Name`.
      
      Returns:
          Sampled tensor of same shape as ``x`` from the Gumbel-Softmax distribution. 
          If ``hard = True``, the returned samples will be one-hot, otherwise they will be 
          probability distributions that sum to 1 across ``axis``.
      
      Examples:
          .. code-block:: python
  
              import paddle
              import paddle.nn.functional as F
  
              logits = paddle.randn([4, 6])
              temperature = 0.01
              gumbel_softmax = F.gumbel_softmax(logits, temperature)
              print(gumbel_softmax)
              # out's value is as follows:
              # [[0.00000001, 1.        , 0.00000000, 0.00000000, 0.00000006, 0.00000000],
              # [0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 1.        ],
              # [0.00000062, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.99999940],
              # [0.00000000, 0.00000000, 0.00000000, 0.00001258, 0.99998736, 0.00000000]]
          
      """
      if in_dynamic_mode():
          return _C_ops.gumbel_softmax(x, 'temperature', temperature, 'hard',
                                       hard, 'axis', axis)
  
      helper = LayerHelper("gumbel_softmax", **locals())
      check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'gumbel_softmax')
      out = helper.create_variable_for_type_inference(x.dtype)
      helper.append_op(
          type='gumbel_softmax',
          inputs={'X': x},
          outputs={'Out': out},
          attrs={'temperature': temperature,
                 'hard': hard,
                 'axis': axis})
      return out
  