code: def efficientnet(
      width_coefficient=None,
      depth_coefficient=None,
      dropout_rate=0.2,
      survival_prob=0.8,
  ):
      """Creates a efficientnet model."""
      global_params = efficientnet_model.GlobalParams(
          blocks_args=_DEFAULT_BLOCKS_ARGS,
          batch_norm_momentum=0.99,
          batch_norm_epsilon=1e-3,
          dropout_rate=dropout_rate,
          survival_prob=survival_prob,
          data_format="channels_last",
          num_classes=1000,
          width_coefficient=width_coefficient,
          depth_coefficient=depth_coefficient,
          depth_divisor=8,
          min_depth=None,
          relu_fn=tf.nn.swish,
          # The default is TPU-specific batch norm.
          # The alternative is tf.layers.BatchNormalization.
          batch_norm=utils.TpuBatchNormalization,  # TPU-specific requirement.
          use_se=True,
          clip_projection_output=False,
      )
      return global_params
  
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 3 for calling a global function with example usage: efficientnet(width_coefficient, depth_coefficient, dropout_rate, survival_prob) and returns: global_params