code: def small_multiple_inputs_outputs_resnet(
      images, test=False, w_bias=False
  ):
      # Branches
      outputs = []
      for i, image in enumerate(images):
          h = image
          h /= 255.0
          h = PF.convolution(
              h,
              16,
              kernel=(3, 3),
              pad=(1, 1),
              with_bias=w_bias,
              name="first-mo-conv-{}".format(i),
          )
          h = PF.batch_normalization(
              h,
              axes=[1],
              batch_stat=not test,
              name="first-mo-bn-{}".format(i),
          )
          h = F.relu(h)
          h = F.max_pooling(h, (2, 2))
          outputs.append(h)
      # Merge branches
      z = sum(outputs)
  
      h = multiple_inputs_outputs_resblock(
          z, maps=16, w_bias=w_bias, test=test, name="mo-cb1"
      )
      h = F.average_pooling(h, (2, 2))
      pred1 = PF.affine(h, 10, name="mo-fc1")
  
      h = multiple_inputs_outputs_resblock(
          z, maps=16, w_bias=w_bias, test=test, name="mo-cb2"
      )
      h = F.average_pooling(h, (2, 2))
      pred2 = PF.affine(h, 10, name="mo-fc2")
      return [pred1, pred2]
  
imports: from __future__ import absolute_import
  import nnabla
  import nnabla.functions
  import nnabla.parametric_functions
  from .helper import create_scale_bias, create_conv_weight_bias, create_affine_weight_bias, get_channel_axes
label_desc: Use image node_id 22 for calling a global function with example usage: small_multiple_inputs_outputs_resnet(images, test, w_bias) and returns: list