code: def process_layer_index_data(layer_ls, layer_name_prefix=""):
      """
      Extract layer names and numpy-like fancy indexing from a string.
      Args:
          layer_ls (list of strs): list of strings containing data about layer names
              and their indexing. For each string, layer name and indexing is separated by whitespaces.
              e.g.: [layer1 1,2;2, layer2, layer3 150;3,4]
          layer_name_prefix (Optional[str]): prefix to be added to each layer name.
      Returns:
          layer_name (list of strings): a list of layer names.
          indexing_dict (Python dict): a dictionary of the pair
              {one_layer_name: indexing_for_that_layer}
      """
  
      layer_name, indexing_dict = [], {}
      for layer in layer_ls:
          ls = layer.split()
          name = layer_name_prefix + ls[0]
          layer_name.append(name)
          if len(ls) == 2:
              indexing_dict[name] = get_indexing(ls[1])
          else:
              indexing_dict[name] = ()
      return layer_name, indexing_dict
  
imports: import itertools
  import numpy
  import matplotlib.pyplot
  import torch
  from sklearn.metrics import confusion_matrix
  import slowfast.utils.logging
  from slowfast.datasets.utils import pack_pathway_output, tensor_normalize
label_desc: Use image node_id 5 for calling a global function with example usage: process_layer_index_data(layer_ls, layer_name_prefix) and returns: layer_name,  indexing_dict