parameters: (x, threshold=1.0, name=None)
return_statement: return out
docstring: 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.]
docstring_summary: r"""
      thresholded relu activation.
docstring_tokens: r thresholded relu activation .
function: 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_dygraph_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
function_tokens: def thresholded_relu ( x , threshold = 1.0 , name = None ) : if in_dygraph_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