code: def build_model_base(
      images, model_name, training, override_params=None
  ):
      """A helper functiion to create a base model and return global_pool.
  
      Args:
        images: input images tensor.
        model_name: string, the predefined model name.
        training: boolean, whether the model is constructed for training.
        override_params: A dictionary of params for overriding. Fields must exist in
          efficientnet_model.GlobalParams.
  
      Returns:
        features: global pool features.
        endpoints: the endpoints for each layer.
  
      Raises:
        When model_name specified an undefined model, raises NotImplementedError.
        When override_params has invalid fields, raises ValueError.
      """
      assert isinstance(images, tf.Tensor)
      # For backward compatibility.
      if override_params and override_params.get(
          "drop_connect_rate", None
      ):
          override_params["survival_prob"] = (
              1 - override_params["drop_connect_rate"]
          )
  
      blocks_args, global_params = get_model_params(
          model_name, override_params
      )
  
      with tf.variable_scope(model_name):
          model = efficientnet_model.Model(blocks_args, global_params)
          features = model(
              images, training=training, features_only=True
          )
  
      features = tf.identity(features, "features")
      return features, model.endpoints
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import functools
  import os
  import re
  from absl import logging
  import numpy
  import six
  import tensorflow.compat.v1
  import efficientnet_model
  import utils
label_desc: Use image node_id 6 for calling a global function with example usage: build_model_base(images, model_name, training, override_params) and returns: features,  model