code: def predict(
      input_sentence, model, classes, cached=False, device="cpu"
  ):
      input_t = model.tokenizer.encode(input_sentence)
      input_t = torch.tensor([input_t], dtype=torch.long, device=device)
      if cached:
          input_t = model.avg_representation(input_t)
  
      log_probs = model(input_t).data.cpu().numpy().flatten().tolist()
      print("Input sentence:", input_sentence)
      print(
          "Predictions:",
          ", ".join(
              "{}: {:.4f}".format(c, math.exp(log_prob))
              for c, log_prob in zip(classes, log_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 a global function with example usage: predict(input_sentence, model, classes, cached, device) without return types