code: def chat_completion(
      self,
      dialogs: List[Dialog],
      temperature: float = 0.6,
      top_p: float = 0.9,
      max_gen_len: Optional[int] = None,
      logprobs: bool = False,
  ) -> List[ChatPrediction]:
      if max_gen_len is None:
          max_gen_len = self.model.params.max_seq_len - 1
      prompt_tokens = []
      unsafe_requests = []
      for dialog in dialogs:
          unsafe_requests.append(
              any(
                  [
                      tag in msg["content"]
                      for tag in SPECIAL_TAGS
                      for msg in dialog
                  ]
              )
          )
          if dialog[0]["role"] == "system":
              dialog = [
                  {
                      "role": dialog[1]["role"],
                      "content": B_SYS
                      + dialog[0]["content"]
                      + E_SYS
                      + dialog[1]["content"],
                  }
              ] + dialog[2:]
          assert all(
              [msg["role"] == "user" for msg in dialog[::2]]
          ) and all(
              [msg["role"] == "assistant" for msg in dialog[1::2]]
          ), (
              "model only supports 'system', 'user' and 'assistant' roles, "
              "starting with 'system', then 'user' and alternating (u/a/u/a/u...)"
          )
          dialog_tokens: List[int] = sum(
              [
                  self.tokenizer.encode(
                      f"{B_INST} {(prompt['content']).strip()} {E_INST} {(answer['content']).strip()} ",
                      bos=True,
                      eos=True,
                  )
                  for prompt, answer in zip(
                      dialog[::2],
                      dialog[1::2],
                  )
              ],
              [],
          )
          assert (
              dialog[-1]["role"] == "user"
          ), f"Last message must be from user, got {dialog[-1]['role']}"
          dialog_tokens += self.tokenizer.encode(
              f"{B_INST} {(dialog[-1]['content']).strip()} {E_INST}",
              bos=True,
              eos=False,
          )
          prompt_tokens.append(dialog_tokens)
  
      generation_tokens, generation_logprobs = self.generate(
          prompt_tokens=prompt_tokens,
          max_gen_len=max_gen_len,
          temperature=temperature,
          top_p=top_p,
          logprobs=logprobs,
      )
      if logprobs:
          return [
              {
                  "generation": {
                      "role": "assistant",
                      "content": self.tokenizer.decode(t)
                      if not unsafe
                      else UNSAFE_ERROR,
                  },
                  "tokens": [self.tokenizer.decode(x) for x in t],
                  "logprobs": logprobs_i,
              }
              for t, logprobs_i, unsafe in zip(
                  generation_tokens,
                  generation_logprobs,
                  unsafe_requests,
              )
          ]
      return [
          {
              "generation": {
                  "role": "assistant",
                  "content": self.tokenizer.decode(t)
                  if not unsafe
                  else UNSAFE_ERROR,
              }
          }
          for t, unsafe in zip(generation_tokens, unsafe_requests)
      ]
  
imports: import json
  import os
  import sys
  import time
  from pathlib import Path
  from typing import List, Literal, Optional, Tuple, TypedDict
  import torch
  import torch.nn.functional
  from fairscale.nn.model_parallel.initialize import get_model_parallel_rank, initialize_model_parallel, model_parallel_is_initialized
  from llama.model import ModelArgs, Transformer
  from llama.tokenizer import Tokenizer
label_desc: Use image node_id 6 for calling the Llama obj's underlying member method code with example usage: obj.chat_completion(dialogs, temperature, top_p, max_gen_len, logprobs) and returns: unknown, unknown