parameters: (features)
return_statement: return features * math_ops.sigmoid(features)
docstring: Computes the Swish activation function: `x * sigmoid(x)`.
  
    Source: "Swish: a Self-Gated Activation Function" (Ramachandran et al. 2017)
    https://arxiv.org/abs/1710.05941
  
    Args:
      features: A `Tensor` representing preactivation values.
      name: A name for the operation (optional).
  
    Returns:
      The activation value.
docstring_summary: Computes the Swish activation function: `x * sigmoid(x)`.
docstring_tokens: Computes the Swish activation function : x * sigmoid ( x ) .
function: def swish(features):
    # pylint: disable=g-doc-args
    """Computes the Swish activation function: `x * sigmoid(x)`.
  
    Source: "Swish: a Self-Gated Activation Function" (Ramachandran et al. 2017)
    https://arxiv.org/abs/1710.05941
  
    Args:
      features: A `Tensor` representing preactivation values.
      name: A name for the operation (optional).
  
    Returns:
      The activation value.
    """
    # pylint: enable=g-doc-args
    features = ops.convert_to_tensor(features, name="features")
    return features * math_ops.sigmoid(features)
function_tokens: def swish ( features ) : # pylint: disable=g-doc-args # pylint: enable=g-doc-args features = ops . convert_to_tensor ( features , name = "features" ) return features * math_ops . sigmoid ( features )