code: def get_teacher_predictions(
      model_path: str,
      examples: List[str],
      class_names: List[str],
      hypothesis_template: str,
      batch_size: int,
      temperature: float,
      multi_label: bool,
      use_fast_tokenizer: bool,
      no_cuda: bool,
      fp16: bool,
  ):
      """
      Gets predictions by the same method as the zero-shot pipeline but with DataParallel & more efficient batching
      """
      model = AutoModelForSequenceClassification.from_pretrained(
          model_path
      )
      model_config = model.config
      if not no_cuda and torch.cuda.is_available():
          model = nn.DataParallel(model.cuda())
          batch_size *= len(model.device_ids)
      tokenizer = AutoTokenizer.from_pretrained(
          model_path, use_fast=use_fast_tokenizer
      )
  
      premises, hypotheses = get_premise_hypothesis_pairs(
          examples, class_names, hypothesis_template
      )
      logits = []
  
      for i in tqdm(range(0, len(premises), batch_size)):
          batch_premises = premises[i : i + batch_size]
          batch_hypotheses = hypotheses[i : i + batch_size]
  
          encodings = tokenizer(
              batch_premises,
              batch_hypotheses,
              padding=True,
              truncation="only_first",
              return_tensors="pt",
          )
  
          with torch.cuda.amp.autocast(enabled=fp16):
              with torch.no_grad():
                  outputs = model(**encodings)
          logits.append(outputs.logits.detach().cpu().float())
  
      entail_id = get_entailment_id(model_config)
      contr_id = -1 if entail_id == 0 else 0
      logits = torch.cat(logits, dim=0)  # N*K x 3
      nli_logits = logits.reshape(len(examples), len(class_names), -1)[
          ..., [contr_id, entail_id]
      ]  # N x K x 2
  
      if multi_label:
          # softmax over (contr, entail) logits for each class independently
          nli_prob = (nli_logits / temperature).softmax(-1)
      else:
          # softmax over entail logits across classes s.t. class probabilities sum to 1.
          nli_prob = (nli_logits / temperature).softmax(1)
  
      return nli_prob[..., 1]
  
imports: import logging
  import os
  import sys
  from dataclasses import dataclass, field
  from typing import List, Optional
  import torch
  from datasets import Dataset
  from torch import nn
  from tqdm.auto import tqdm
  from transformers import AutoModelForSequenceClassification, AutoTokenizer, HfArgumentParser, Trainer, TrainingArguments, set_seed, utils
  from transformers.trainer_utils import get_last_checkpoint, is_main_process
label_desc: Use image node_id 4 for calling a global function with example usage: get_teacher_predictions(model_path, examples, class_names, hypothesis_template, batch_size, temperature, multi_label, use_fast_tokenizer, no_cuda, fp16) and returns: nli_prob