code: def train_epoch(model, train_dataloader, loss_func, optimizer):
      model.train()
      loss_all = []
      for step, inputs in enumerate(train_dataloader):
          if cuda:
              inputs = inputs.cuda()
          logits = model(inputs)
          labels = inputs["label"]
          loss = loss_func(logits, labels)
          loss.backward()
          loss_all.append(loss.item())
          optimizer.step()
          optimizer.zero_grad()
      return np.mean(loss_all)
  
imports: from openprompt.data_utils.text_classification_dataset import SST2Processor
  from openprompt.plms import load_plm
  from openprompt.prompts import ManualVerbalizer, ManualTemplate
  from openprompt.prompts.prompt_generator import LMBFFTemplateGenerationTemplate
  from openprompt.plms import load_plm
  from openprompt.prompts.prompt_generator import T5TemplateGenerator
  from openprompt.pipeline_base import PromptDataLoader, PromptForClassification
  from openprompt.prompts import ManualTemplate
  from openprompt.trainer import ClassificationRunner
  import copy
  import torch
  from transformers import AdamW, get_linear_schedule_with_warmup
  import numpy
  from tqdm import tqdm
  from openprompt.prompts.prompt_generator import RobertaVerbalizerGenerator
label_desc: Use image node_id 2 for calling a global function with example usage: train_epoch(model, train_dataloader, loss_func, optimizer) and returns: np