code: def __init__(self, train_dir=None, eval_dir=None):
      task_name = "rte"
      class_num = 2
      feature_column = ["sentence1", "sentence2"]
      label_column = "label"
      metric = [Accuracy()]
      super(RTE, self).__init__(
          task_name,
          class_num,
          feature_column,
          label_column,
          metric,
          train_dir,
          eval_dir,
      )
  
imports: import gluonnlp
  import numpy
  import mxnet
  import pandas
  import matplotlib.pyplot
  from tqdm import tqdm
  import copy
  from mxnet.gluon import nn
  from mxnet.gluon.metric import Accuracy, F1, MCC, PearsonCorrelation, CompositeEvalMetric
  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 RTE object from inherited base classes: GlueTask with example: obj = RTE(train_dir, eval_dir)