code: def get_net(model_name, context):
      if model_name == "FCN":
          num_classes = 21
          pretrained_net = vision.resnet18_v2(
              pretrained=True, ctx=context
          )
          net = gluon.nn.HybridSequential()
          for layer in pretrained_net.features[:-2]:
              net.add(layer)
          net.add(
              gluon.nn.Conv2D(num_classes, kernel_size=1),
              gluon.nn.Conv2DTranspose(
                  num_classes, kernel_size=64, padding=16, strides=32
              ),
          )
          net[-1].initialize(
              init.Constant(
                  bilinear_kernel(num_classes, num_classes, 64)
              ),
              ctx=context,
          )
          net[-2].initialize(init=init.Xavier(), ctx=context)
          input_shape = (1, 3, 320, 480)
          label_shape = (1, 320, 480)
          loss_axis = 1
      else:
          net = vision.get_model(model_name, classes=10)
          net.initialize(mx.init.Xavier(), ctx=context)
          input_shape = (1, 1, 224, 224)
          label_shape = 1
          loss_axis = -1
      return net, input_shape, label_shape, loss_axis
  
file_path: mxnet/tests/nightly/estimator/test_estimator_cnn.py
imports: import os
  import sys
  import mxnet
  import numpy
  from mxnet import gluon, init, nd
  from mxnet.gluon import data
  from mxnet.gluon.contrib.estimator import estimator
  from mxnet.gluon.model_zoo import vision
  import pytest
label_desc: Use image node_id 3 for calling a global function with example usage: get_net(model_name, context) and returns: net,  input_shape,  label_shape,  loss_axis