parameters: (x, name=None)
return_statement: return out
docstring: 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]
docstring_summary: r"""
      mish activation.
docstring_tokens: r mish activation .
function: 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.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
function_tokens: def mish ( x , name = None ) : if in_dygraph_mode ( ) : 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