class_docstr: Transformer encoder followed by a Classification Head
class_docstr_tok: ["Transformer","encoder","followed","by","a","Classification","Head"]
code: def train_custom(self):
      for param in self.encoder.parameters():
          param.requires_grad = False
      self.classifier_head.train()
  
imports: import argparse
  import csv
  import json
  import math
  import time
  import numpy
  import torch
  import torch.optim
  import torch.utils.data
  from nltk.tokenize.treebank import TreebankWordDetokenizer
  from torch import nn
  from torchtext import data
  from torchtext import datasets
  from tqdm import tqdm, trange
  from pplm_classification_head import ClassificationHead
  from transformers import GPT2LMHeadModel, GPT2Tokenizer
label_desc: Use image node_id 3 for calling the Discriminator obj's underlying member method code with example usage: obj.train_custom() without return types