code: def get_deriv(identifier):
      """get the activation derivative function"""
      if identifier is None:
          return linear
      if callable(identifier):
          raise NotImplementedError(
              "During training, please use a string "
              '(e.g "relu") to specify the activation '
              "function instead of calling directly "
              "the activation function."
          )
      if isinstance(identifier, str):
          activations = {
              "sigmoid": sigmoid_deriv,
              "relu": relu_deriv,
              "linear": linear_deriv,
          }
          if identifier not in activations.keys():
              raise NotImplementedError(
                  "Activation function {} not yet implemented "
                  "during training".format(identifier)
              )
          return activations[identifier]
  
      raise ValueError(
          "Could not interpret activation function identifier:",
          identifier,
      )
  
file_path: tf-encrypted/tf_encrypted/keras/activations.py
imports: import tf_encrypted
label_desc: Use image node_id 10 for calling a global function with example usage: get_deriv(identifier) and returns: linear, activations