code: def _get_path_for_branching_from_start_layer(
      nn, start_layer, min_path_length=4, end_path_prob=0.20
  ):
      """Returns a path which starts at start layer."""
      path = [start_layer]
      while True:
          curr_layer = path[-1]
          curr_children = nn.get_children(curr_layer)
          next_layer = int(np.random.choice(curr_children, 1))
          path.append(next_layer)
          if nn.layer_labels[next_layer] == "op":
              break
          elif len(path) < min_path_length:
              pass
          elif np.random.random() < end_path_prob:
              break
      return path
  
imports: from argparse import Namespace
  from copy import deepcopy
  import numpy
  from .neural_network import ConvNeuralNetwork, MultiLayerPerceptron, MLP_RECTIFIERS, MLP_SIGMOIDS, is_a_pooling_layer_label, is_a_conv_layer_label, CNNImageSizeMismatchException, CNNNoConvAfterIPException
  from ..utils.general_utils import reorder_list_or_array, reorder_rows_and_cols_in_matrix
  from ..utils.option_handler import get_option_specs, load_options
  from ..utils.reporters import get_reporter
label_desc: Use image node_id 11 for calling a global function with example usage: _get_path_for_branching_from_start_layer(nn, start_layer, min_path_length, end_path_prob) and returns: path