code: def get_network(
      model_name,
      ctx_l,
      checkpoint_path=None,
      backbone_path=None,
      task=None,
  ):
      """
      Get the network that fine-tune the Question Answering Task
      """
  
      use_segmentation = (
          "roberta" not in model_name and "xlmr" not in model_name
      )
      Model, cfg, tokenizer, download_params_path, _ = get_backbone(
          model_name, load_backbone=not backbone_path
      )
      backbone = Model.from_cfg(cfg)
      # Load local backbone parameters if backbone_path provided.
      # Otherwise, download backbone parameters from gluon zoo.
  
      backbone_params_path = (
          backbone_path if backbone_path else download_params_path
      )
      if checkpoint_path is None:
          backbone.load_parameters(
              backbone_params_path,
              ignore_extra=True,
              ctx=ctx_l,
              cast_dtype=True,
          )
          num_params, num_fixed_params = count_parameters(
              deduplicate_param_dict(backbone.collect_params())
          )
          logging.info(
              "Loading Backbone Model from {}, with total/fixd parameters={}/{}".format(
                  backbone_params_path, num_params, num_fixed_params
              )
          )
      classify_net = TextPredictionNet(backbone, task.class_num)
      if checkpoint_path is None:
          # Ignore the UserWarning during initialization,
          # There is no need to re-initialize the parameters of backbone
          classify_net.initialize(ctx=ctx_l)
      else:
          classify_net.load_parameters(
              checkpoint_path, ctx=ctx_l, cast_dtype=True
          )
      classify_net.hybridize()
  
      return cfg, tokenizer, classify_net, use_segmentation
  
imports: import gluonnlp
  from tensorboardX import SummaryWriter
  import numpy
  import mxnet
  import json
  import random
  import pandas
  import os
  import logging
  import time
  import argparse
  import copy
  from mxnet.gluon.metric import Accuracy, F1, MCC, PearsonCorrelation, CompositeEvalMetric
  from classification_utils import get_task
  import matplotlib.pyplot
  from tqdm import tqdm
  from mxnet import gluon
  from gluonnlp.data.sampler import SplitSampler
  from mxnet.gluon import nn
  from gluonnlp.models import get_backbone
  from gluonnlp.utils.parameter import clip_grad_global_norm, count_parameters, deduplicate_param_dict
  from gluonnlp.utils.preprocessing import get_trimmed_lengths
  from gluonnlp.utils.misc import get_mxnet_visible_ctx, grouper, repeat, logging_config
  from mxnet.gluon.data import batchify
  from mxnet.gluon.data import DataLoader
  from mxnet.lr_scheduler import PolyScheduler
  from gluonnlp.utils import set_seed
  from gluonnlp.utils.misc import init_comm, parse_ctx
  from classification import TextPredictionNet
label_desc: Use image node_id 2 for calling a global function with example usage: get_network(model_name, ctx_l, checkpoint_path, backbone_path, task) and returns: cfg,  tokenizer,  classify_net,  use_segmentation