code: def inception_resnet_v2_arg_scope(
      weight_decay=0.00004,
      batch_norm_decay=0.9997,
      batch_norm_epsilon=0.001,
      activation_fn=tf.nn.relu,
  ):
      """Returns the scope with the default parameters for inception_resnet_v2.
  
      Args:
        weight_decay: the weight decay for weights variables.
        batch_norm_decay: decay for the moving average of batch_norm momentums.
        batch_norm_epsilon: small float added to variance to avoid dividing by zero.
        activation_fn: Activation function for conv2d.
  
      Returns:
        a arg_scope with the parameters needed for inception_resnet_v2.
      """
      # Set weight_decay for weights in conv2d and fully_connected layers.
      with slim.arg_scope(
          [slim.conv2d, slim.fully_connected],
          weights_regularizer=slim.l2_regularizer(weight_decay),
          biases_regularizer=slim.l2_regularizer(weight_decay),
      ):
          batch_norm_params = {
              "decay": batch_norm_decay,
              "epsilon": batch_norm_epsilon,
              "fused": None,  # Use fused batch norm if possible.
          }
          # Set activation_fn and parameters for batch_norm.
          with slim.arg_scope(
              [slim.conv2d],
              activation_fn=activation_fn,
              normalizer_fn=slim.batch_norm,
              normalizer_params=batch_norm_params,
          ) as scope:
              return scope
  
file_path: MMdnn/mmdnn/conversion/examples/tensorflow/models/inception_resnet_v2.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow
label_desc: Use image node_id 6 for calling a global function with example usage: inception_resnet_v2_arg_scope(weight_decay, batch_norm_decay, batch_norm_epsilon, activation_fn) and returns: scope