code: def generate_mlp_architectures(class_or_reg="reg"):
      """pylint: disable=bad-whitespace."""
      # pylint: disable=bad-whitespace
      all_layer_label_classes = get_mlp_layer_labels(class_or_reg)
      last_layer_label = (
          "linear" if class_or_reg == "reg" else "softmax"
      )
      # Network 1
      layer_labels = [
          "ip",
          "op",
          "tanh",
          "logistic",
          "softplus",
          "relu",
          "elu",
          last_layer_label,
      ]
      num_units_each_layer = [None, None, 32, 64, 16, 8, 8, None]
      A = get_dok_mat_with_set_coords(
          8,
          [
              (0, 4),
              (2, 3),
              (3, 7),
              (4, 5),
              (4, 6),
              (5, 2),
              (6, 2),
              (7, 1),
          ],
      )
      mlp_1 = MultiLayerPerceptron(
          class_or_reg,
          layer_labels,
          A,
          num_units_each_layer,
          all_layer_label_classes,
      )
      # Network 2
      layer_labels = [
          "ip",
          "softplus",
          "elu",
          "tanh",
          "logistic",
          last_layer_label,
          "op",
      ]
      num_units_each_layer = [None, 16, 16, 32, 64, None, None]
      A = get_dok_mat_with_set_coords(
          7, [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]
      )
      mlp_2 = MultiLayerPerceptron(
          class_or_reg,
          layer_labels,
          A,
          num_units_each_layer,
          all_layer_label_classes,
      )
      # Network 3
      layer_labels = [
          "ip",
          "tanh",
          "logistic",
          "logistic",
          "tanh",
          "elu",
          "relu",
          last_layer_label,
          "op",
      ]
      num_units_each_layer = [None, 8, 8, 8, 8, 16, 16, None, None]
      A = get_dok_mat_with_set_coords(
          9,
          [
              (0, 1),
              (0, 2),
              (1, 3),
              (2, 4),
              (3, 5),
              (3, 6),
              (4, 5),
              (4, 6),
              (5, 7),
              (6, 7),
              (7, 8),
          ],
      )
      mlp_3 = MultiLayerPerceptron(
          class_or_reg,
          layer_labels,
          A,
          num_units_each_layer,
          all_layer_label_classes,
      )
      # Network 4
      layer_labels = [
          "ip",
          "logistic",
          "relu",
          "softplus",
          last_layer_label,
          "op",
          "relu",
          "tanh",
      ]
      num_units_each_layer = [None, 8, 8, 16, None, None, 16, 8]
      A = get_dok_mat_with_set_coords(
          8,
          [
              (0, 1),
              (0, 7),
              (1, 2),
              (2, 3),
              (2, 6),
              (7, 6),
              (7, 3),
              (3, 4),
              (6, 4),
              (4, 5),
          ],
      )
      mlp_4 = MultiLayerPerceptron(
          class_or_reg,
          layer_labels,
          A,
          num_units_each_layer,
          all_layer_label_classes,
      )
      return [
          mlp_1,
          mlp_2,
          mlp_3,
          mlp_4,
          get_blocked_mlp(class_or_reg, 4, 3),
          get_blocked_mlp(class_or_reg, 8, 2),
          get_multidepth_mlp_eg1(class_or_reg),
          get_multidepth_mlp_eg2(class_or_reg),
      ]
  
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 15 for calling a global function with example usage: generate_mlp_architectures(class_or_reg) and returns: list