code: def _create_model(input_shape, classes):
      image = Input(input_shape)
      lambda_layer = Lambda(_imagenet_preprocess_tf)
      preprocessed_image = lambda_layer(image)
      model = vgg16.VGG16(
          classes=classes,
          input_tensor=preprocessed_image,
          weights=None,
          include_top=False,
      )
  
      x = Flatten(name="flatten")(model.output)
      x = Dense(4096, activation="relu", name="fc1")(x)
      x = Dense(4096, activation="relu", name="fc2")(x)
      x = Dense(classes, activation="softmax", name="predictions")(x)
      return Model(inputs=model.input, outputs=x)
  
imports: import math
  import os
  import tarfile
  import click
  import keras
  import numpy
  import tensorflow
  from image_pyfunc import decode_and_resize_image, log_model
  from keras.applications import vgg16
  from keras.callbacks import Callback
  from keras.layers import Dense, Flatten, Input, Lambda
  from keras.models import Model
  from keras.utils import np_utils
  from sklearn.model_selection import train_test_split
  import mlflow
  from mlflow.models import infer_signature
label_desc: Use image node_id 4 for calling a global function with example usage: _create_model(input_shape, classes) and returns: Model