class_docstr: Transformer encoder followed by a Classification Head
class_docstr_tok: ["Transformer","encoder","followed","by","a","Classification","Head"]
code: def forward(self, x):
      if self.cached_mode:
          avg_hidden = x.to(self.device)
      else:
          avg_hidden = self.avg_representation(x.to(self.device))
  
      logits = self.classifier_head(avg_hidden)
      probs = nn.functional.log_softmax(logits, dim=-1)
  
      return probs
  
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 pplm_classification_head import ClassificationHead
  from torch import nn
  from torchtext import data
  from torchtext import datasets
  from tqdm import tqdm, trange
  from transformers import GPT2LMHeadModel, GPT2Tokenizer
label_desc: Use image node_id 5 for calling the Discriminator obj's underlying member method code with example usage: obj.forward(x) and returns: probs