code: def infer(
      self, context, top_p=0.9, top_k=40, temp=1.0, length=256, **kwargs
  ):
      item = self.prepare_item(context, length)
      batched_tokens = np.array([item["tokens"]] * self.total_batch)
      batched_lengths = np.array([item["length"]] * self.total_batch)
      start = timer()
      output = self.network.generate(
          batched_tokens,
          batched_lengths,
          length,
          {
              "top_p": np.ones(self.total_batch) * top_p,
              "top_k": np.ones(self.total_batch) * top_k,
              "temp": np.ones(self.total_batch) * temp,
          },
      )
      samples = []
      decoded_tokens = output[1][0]
      end_time = timer(start)
      for o in decoded_tokens[:, :, 0]:
          res = {
              "context": context,
              "completion": self.tokenizer.decode(o),
              "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 5 for calling the GPTJ obj's underlying member method code with example usage: obj.infer(context, top_p, top_k, temp, length) and returns: samples