class_docstr: Unit tests for general NN functions. 
docstr: Tests labels generated for a CNN and MLP. 
docstr_tok: ["Tests","labels","generated","for","a","CNN","and","MLP","."]
code: def test_get_labels(self):
      """Tests labels generated for a CNN and MLP."""
      self.report("Testing the labels for CNN and MLP.")
      cnn_true_labels_7 = sorted(
          [
              "ip",
              "op",
              "fc",
              "max-pool",
              "avg-pool",
              "softmax",
              "res3",
              "res5",
              "res7",
              "conv3",
              "conv5",
              "conv7",
          ]
      )
      cnn_true_labels_3 = sorted(
          [
              "ip",
              "op",
              "fc",
              "max-pool",
              "avg-pool",
              "softmax",
              "res3",
              "conv3",
          ]
      )
      mlp_true_labels = sorted(["ip", "op", "relu", "linear"])
      cnn_ret_labels_7 = sorted(neural_network.get_cnn_layer_labels(7))
      cnn_ret_labels_3 = sorted(neural_network.get_cnn_layer_labels(3))
      mlp_ret_labels = sorted(
          neural_network.get_mlp_layer_labels("reg", ["relu"])
      )
      assert cnn_true_labels_7 == cnn_ret_labels_7
      assert cnn_true_labels_3 == cnn_ret_labels_3
      assert mlp_true_labels == mlp_ret_labels
  
file_path: dragonfly/dragonfly/nn/unittest_neural_network.py
imports: import numpy
  from .None import neural_network
  from .nn_examples import generate_cnn_architectures, generate_mlp_architectures
  from ..utils.base_test_class import BaseTestClass, execute_tests
  from ..utils.graph_utils import apsp_floyd_warshall_costs
label_desc: Use image node_id 2 for calling the GeneralNNFunctionsTestCase obj's underlying member method code with example usage: obj.test_get_labels() without return types