code: def default_param_noise_filter(var):
      """
      check whether or not a variable is perturbable or not
  
      :param var: (TensorFlow Tensor) the variable
      :return: (bool) can be perturb
      """
      if var not in tf.trainable_variables():
          # We never perturb non-trainable vars.
          return False
      if "fully_connected" in var.name:
          # We perturb fully-connected layers.
          return True
  
      # The remaining layers are likely conv or layer norm layers, which we do not wish to
      # perturb (in the former case because they only extract features, in the latter case because
      # we use them for normalization purposes). If you change your network, you will likely want
      # to re-consider which layers to perturb and which to keep untouched.
      return False
  
file_path: stable-baselines/stable_baselines/deepq/build_graph.py
imports: import tensorflow
  from gym.spaces import MultiDiscrete
  from stable_baselines.common import tf_util
label_desc: Use image node_id 4 for calling a global function with example usage: default_param_noise_filter(var) and returns: False, False, True