code: def __init__(self, model_name, num_train_steps, learning_rate):
      super().__init__()
      self.num_train_steps = num_train_steps
      self.learning_rate = learning_rate
      hidden_dropout_prob: float = 0.1
      layer_norm_eps: float = 1e-7
  
      config = AutoConfig.from_pretrained(model_name)
  
      config.update(
          {
              "output_hidden_states": True,
              "hidden_dropout_prob": hidden_dropout_prob,
              "layer_norm_eps": layer_norm_eps,
              "add_pooling_layer": False,
              "num_labels": 1,
          }
      )
      self.transformer = AutoModel.from_pretrained(
          model_name, config=config
      )
      self.dropout = nn.Dropout(config.hidden_dropout_prob)
      self.output = nn.Linear(config.hidden_size, 1)
  
file_path: tez/examples/nlp/imdb_sentiment_classification.py
imports: import pandas
  import torch
  import torch.nn
  from sklearn import metrics, model_selection
  from transformers import AutoConfig, AutoModel, AutoTokenizer, get_linear_schedule_with_warmup
  from tez import Tez, TezConfig
  from tez.callbacks import EarlyStopping
label_desc: Use image node_id 1 to create a new IMDBModel object from inherited base classes: nn with example: obj = IMDBModel(model_name, num_train_steps, learning_rate)