code: def infer_batch(self, batch, **kwargs):
      logger.info(f"Starting Inference on Batch")
      batch_items = {
          "tokens": [],
          "lengths": [],
          "top_p": [],
          "top_k": [],
          "temp": [],
      }
      max_lengths, contexts = [], []
      for req in batch:
          req = self.to_data(req)
          item = self.prepare_item(req["context"], req["length"])
          batch_items["tokens"].append(item["tokens"])
          batch_items["lengths"].append(item["length"])
          batch_items["top_p"].append(req["top_p"])
          batch_items["top_k"].append(req["top_k"])
          batch_items["temp"].append(req["temp"])
          max_lengths.append(req["length"])
          contexts.append(req["context"])
  
      max_length = max(max_lengths)
      for key, vals in batch_items.items():
          batch_items[key] = np.array(vals)
      start = timer()
      logger.info(f"Completed Preparing Batch")
      output = self.network.generate(
          batch_items["tokens"],
          batch_items["lengths"],
          max_length,
          {
              "top_p": batch_items["top_p"],
              "top_k": batch_items["top_k"],
              "temp": batch_items["temp"],
          },
      )
      logger.info(f"Completed Generation")
      samples = []
      end_time = timer(start)
      for pred, ctx in zip(output[1][0][:, :, 0], contexts):
          res = {
              "context": ctx,
              "completion": self.tokenizer.decode(pred),
              "time": end_time,
          }
          samples.append(res)
      logger.info(f"Completion done in {end_time:06} secs")
      return samples
  
imports: import os
  import time
  import jax
  import optax
  import threading
  import numpy
  import logging
  from queue import Queue, Empty
  from jax.experimental import maps
  from transformers import GPT2TokenizerFast
  from mesh_transformer.checkpoint import read_ckpt
  from mesh_transformer.sampling import nucleaus_sample
  from mesh_transformer.transformer_shard import CausalTransformer
label_desc: Use image node_id 6 for calling the GPTJ obj's underlying member method code with example usage: obj.infer_batch(batch) and returns: samples