code: def nasnet_mobile_arg_scope(
      weight_decay=4e-5,
      batch_norm_decay=0.9997,
      batch_norm_epsilon=1e-3,
  ):
      """Defines the default arg scope for the NASNet-A Mobile ImageNet model.
  
      Args:
        weight_decay: The weight decay to use for regularizing the model.
        batch_norm_decay: Decay for batch norm moving average.
        batch_norm_epsilon: Small float added to variance to avoid dividing by zero
          in batch norm.
  
      Returns:
        An `arg_scope` to use for the NASNet Mobile Model.
      """
      batch_norm_params = {
          # Decay for the moving averages.
          "decay": batch_norm_decay,
          # epsilon to prevent 0s in variance.
          "epsilon": batch_norm_epsilon,
          "scale": True,
          "fused": True,
      }
      weights_regularizer = tf.contrib.layers.l2_regularizer(
          weight_decay
      )
      weights_initializer = (
          tf.contrib.layers.variance_scaling_initializer(mode="FAN_OUT")
      )
      with arg_scope(
          [slim.fully_connected, slim.conv2d, slim.separable_conv2d],
          weights_regularizer=weights_regularizer,
          weights_initializer=weights_initializer,
      ):
          with arg_scope(
              [slim.fully_connected], activation_fn=None, scope="FC"
          ):
              with arg_scope(
                  [slim.conv2d, slim.separable_conv2d],
                  activation_fn=None,
                  biases_initializer=None,
              ):
                  with arg_scope(
                      [slim.batch_norm], **batch_norm_params
                  ) as sc:
                      return sc
  
file_path: MMdnn/mmdnn/conversion/examples/tensorflow/models/nasnet.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow
  from .None import nasnet_utils
label_desc: Use image node_id 5 for calling a global function with example usage: nasnet_mobile_arg_scope(weight_decay, batch_norm_decay, batch_norm_epsilon) and returns: sc