code: def _imagenet_stem(inputs, hparams, stem_cell):
      """Stem used for models trained on ImageNet."""
      num_stem_cells = 2
  
      # 149 x 149 x 32
      num_stem_filters = int(32 * hparams.stem_multiplier)
      net = slim.conv2d(
          inputs,
          num_stem_filters,
          [3, 3],
          stride=2,
          scope="conv0",
          padding="VALID",
      )
      net = slim.batch_norm(net, scope="conv0_bn")
  
      # Run the reduction cells
      cell_outputs = [None, net]
      filter_scaling = 1.0 / (
          hparams.filter_scaling_rate**num_stem_cells
      )
      for cell_num in range(num_stem_cells):
          net = stem_cell(
              net,
              scope="cell_stem_{}".format(cell_num),
              filter_scaling=filter_scaling,
              stride=2,
              prev_layer=cell_outputs[-2],
              cell_num=cell_num,
          )
          cell_outputs.append(net)
          filter_scaling *= hparams.filter_scaling_rate
      return net, cell_outputs
  
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 8 for calling a global function with example usage: _imagenet_stem(inputs, hparams, stem_cell) and returns: net,  cell_outputs