code: def _get_multidepth_mlp_eg12_common(class_or_reg):
      """A network with 2 linear layers mostly for debugging common operations."""
      mlp_layer_labels = get_mlp_layer_labels(class_or_reg)
      obtl_label = "linear" if class_or_reg == "reg" else "softmax"
      layer_labels = [
          "ip",
          obtl_label,
          "op",
          "tanh",
          "relu",
          "leaky-relu",
          "logistic",
          "logistic",
          "elu",
          obtl_label,
      ]
      num_units_in_each_layer = [
          None,
          None,
          None,
          64,
          64,
          128,
          256,
          64,
          512,
          None,
      ]
      edges = [
          (0, 1),
          (0, 3),
          (0, 4),
          (3, 5),
          (4, 7),
          (5, 6),
          (7, 8),
          (3, 8),
          (7, 6),
          (6, 9),
          (8, 9),
          (1, 2),
          (9, 2),
      ]
      return (
          layer_labels,
          num_units_in_each_layer,
          edges,
          mlp_layer_labels,
      )
  
imports: import numpy
  from scipy.sparse import dok_matrix
  from .neural_network import ConvNeuralNetwork, MultiLayerPerceptron, get_cnn_layer_labels, get_mlp_layer_labels, is_a_conv_layer_label
  from .nn_modifiers import NNModifier
  from ..utils.general_utils import get_dok_mat_with_set_coords
label_desc: Use image node_id 11 for calling a global function with example usage: _get_multidepth_mlp_eg12_common(class_or_reg) and returns: layer_labels,  num_units_in_each_layer,  edges,  mlp_layer_labels