code: def test_classifier_no_name(self):
      np.random.seed(1988)
  
      input_dim = 5
      num_hidden = 12
      num_classes = 6
      input_length = 3
  
      model = Sequential()
      model.add(
          LSTM(
              num_hidden,
              input_dim=input_dim,
              input_length=input_length,
              return_sequences=False,
          )
      )
      model.add(Dense(num_classes, activation="softmax"))
  
      model.set_weights(
          [np.random.rand(*w.shape) for w in model.get_weights()]
      )
  
      input_names = ["input"]
      output_names = ["zzzz"]
      class_labels = ["a", "b", "c", "d", "e", "f"]
      predicted_feature_name = "pf"
      coremlmodel = keras_converter.convert(
          model,
          input_names,
          output_names,
          class_labels=class_labels,
          predicted_feature_name=predicted_feature_name,
      )
  
      if _is_macos() and _macos_version() >= (10, 13):
          inputs = np.random.rand(input_dim)
          outputs = coremlmodel.predict({"input": inputs})
          # this checks that the dictionary got the right name and type
          self.assertEquals(
              type(outputs[output_names[0]]), type({"a": 0.5})
          )
  
imports: import unittest
  import numpy
  import tempfile
  import pytest
  import shutil
  import os
  import coremltools
  from coremltools._deps import _HAS_KERAS_TF, MSG_KERAS1_NOT_FOUND
  from coremltools._deps import _HAS_TF, MSG_TF1_NOT_FOUND
  from coremltools.models.utils import _get_custom_layer_names, _replace_custom_layer_name, _macos_version, _is_macos
  from coremltools.proto import Model_pb2
label_desc: Use image node_id 2 for calling the KerasBasicNumericCorrectnessTest obj's underlying member method code with example usage: obj.test_classifier_no_name() without return types