code: def __init__(self, backbone, output_size=2):
      super().__init__()
      self.backbone = backbone
      self.output_size = output_size
      self.out_proj = nn.Dense(
          in_units=backbone.units, units=self.output_size, flatten=False
      )
  
imports: import gluonnlp
  import numpy
  import mxnet
  import pandas
  import matplotlib.pyplot
  from gluonnlp.data.sampler import SplitSampler
  from tqdm import tqdm
  from mxnet.gluon import nn
  from gluonnlp.models import get_backbone
  from gluonnlp.utils.parameter import clip_grad_global_norm
  from gluonnlp.utils.preprocessing import get_trimmed_lengths
  from gluonnlp.utils.misc import get_mxnet_visible_ctx, grouper, repeat
  from mxnet.gluon.data import batchify
  from mxnet.gluon.data import DataLoader
  from mxnet.lr_scheduler import PolyScheduler
  from gluonnlp.utils import set_seed
label_desc: Use image node_id 1 to create a new TextPredictionNet object from inherited base classes: nn with example: obj = TextPredictionNet(backbone, output_size)