code: def build_model(self):
      self.model = self.models[self.model_type](
          weights=self.pretrained_weights, include_top=False
      )
  
      if self.output_classes is not None:
          if self.model_type in ["vgg19", "vgg16"]:
              self.model.layers.pop()
              self.model.outputs = [self.model.layers[-1].output]
              self.model.layers[-1].outbound_nodes = []
              self.model.add(
                  Dense(self.output_classes, activation="softmax")
              )
  
          elif self.model_type in ["inception"]:
              model_output = self.model.output
              model_output = GlobalAveragePooling2D()(model_output)
              model_output = Dense(
                  self.finetuning_dense_features, activation="relu"
              )(model_output)
              predictions = Dense(
                  self.output_classes, activation="softmax"
              )(model_output)
              self.model = Model(self.model.input, predictions)
          elif self.model_type in ["resnet50"]:
              # self.model.layers.pop()
              model_output = self.model.output
              model_output = GlobalAveragePooling2D()(model_output)
              predictions = Dense(
                  self.output_classes, activation="softmax"
              )(model_output)
              self.model = Model(self.model.input, predictions)
  
      if self.bottleneck_layers_num is not None:
          for layer in self.model.layers[: self.bottleneck_layers_num]:
              layer.trainable = False
  
      self.inputs = self.model.input
  
      super(KerasPreTrainedModel, self).build_model(
          compute_output=False
      )
  
file_path: DeepNeuro/deepneuro/models/keras_pretrained.py
imports: from keras.applications import ResNet50
  from keras.applications import InceptionV3
  from keras.applications import Xception
  from keras.applications import VGG16
  from keras.applications import VGG19
  from keras.layers import Dense, GlobalAveragePooling2D
  from keras.engine import Model
  from deepneuro.models.keras_model import KerasModel
  from deepneuro.utilities.util import add_parameter
label_desc: Use image node_id 2 for calling the KerasPreTrainedModel obj's underlying member method code with example usage: obj.build_model() without return types