code: def lenet(image, test=False, w_bias=False):
      h = PF.convolution(
          image, 16, (5, 5), (1, 1), with_bias=False, name="conv1"
      )
      h = PF.batch_normalization(
          h, batch_stat=not test, name="conv1-bn"
      )
      h = F.max_pooling(h, (2, 2))
      h = F.relu(h)
  
      h = PF.convolution(
          h, 16, (5, 5), (1, 1), with_bias=True, name="conv2"
      )
      h = PF.batch_normalization(
          h, batch_stat=not test, name="conv2-bn"
      )
      h = F.max_pooling(h, (2, 2))
      h = F.relu(h)
  
      h = PF.affine(h, 10, with_bias=False, name="fc1")
      h = F.relu(h)
  
      pred = PF.affine(h, 10, with_bias=True, name="fc2")
      return pred
  
file_path: nnabla/python/test/utils/test_graph_converters/ref_graphs/lenets.py
imports: from __future__ import absolute_import
  import nnabla
  import nnabla.functions
  import nnabla.parametric_functions
  from nnabla.parameter import get_parameter_or_create
  from .helper import get_channel_axes
label_desc: Use image node_id 1 for calling a global function with example usage: lenet(image, test, w_bias) and returns: pred