code: def evaluate(prompt_model, dataloader):
      generated_sentence = []
      groundtruth_sentence = []
      prompt_model.eval()
  
      for step, inputs in enumerate(dataloader):
          if use_cuda:
              inputs = inputs.cuda()
          _, output_sentence = prompt_model.generate(
              inputs, **generation_arguments
          )
          generated_sentence.extend(output_sentence)
          groundtruth_sentence.extend(inputs["tgt_text"])
      score = generation_metric(
          generated_sentence, groundtruth_sentence, "sentence_bleu"
      )
      print("test_score", score, flush=True)
      return generated_sentence
  
file_path: OpenPrompt/tutorial/2.1_conditional_generation.py
imports: import argparse
  import torch
  from openprompt.data_utils.conditional_generation_dataset import WebNLGProcessor
  from openprompt.plms import load_plm
  from openprompt.prompts.prefix_tuning_template import PrefixTuningTemplate
  from openprompt import PromptDataLoader
  from openprompt import PromptForGeneration
  from transformers import AdamW
  from transformers.optimization import get_linear_schedule_with_warmup
  from openprompt.utils.metrics import generation_metric
label_desc: Use image node_id 1 for calling a global function with example usage: evaluate(prompt_model, dataloader) and returns: generated_sentence