docstr: Generate assistant responses for a list of conversational dialogs using the language generation model.
  
  Args:
      dialogs (List[Dialog]): List of conversational dialogs, where each dialog is a list of messages.
      temperature (float, optional): Temperature value for controlling randomness in sampling. Defaults to 0.6.
      top_p (float, optional): Top-p probability threshold for nucleus sampling. Defaults to 0.9.
      max_gen_len (Optional[int], optional): Maximum length of the generated response sequence.
          If not provided, it's set to the model's maximum sequence length minus 1.
      logprobs (bool, optional): Flag indicating whether to compute token log probabilities. Defaults to False.
  
  Returns:
      List[ChatPrediction]: List of chat predictions, each containing the assistant's generated response.
  
  Raises:
      AssertionError: If the last message in a dialog is not from the user.
      AssertionError: If the dialog roles are not in the required 'user', 'assistant', and optional 'system' order.
  
  Note:
      This method generates assistant responses for the provided conversational dialogs.
      It employs nucleus sampling to introduce controlled randomness in text generation.
      If logprobs is True, token log probabilities are computed for each generated token.
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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]:
      """
      Generate assistant responses for a list of conversational dialogs using the language generation model.
  
      Args:
          dialogs (List[Dialog]): List of conversational dialogs, where each dialog is a list of messages.
          temperature (float, optional): Temperature value for controlling randomness in sampling. Defaults to 0.6.
          top_p (float, optional): Top-p probability threshold for nucleus sampling. Defaults to 0.9.
          max_gen_len (Optional[int], optional): Maximum length of the generated response sequence.
              If not provided, it's set to the model's maximum sequence length minus 1.
          logprobs (bool, optional): Flag indicating whether to compute token log probabilities. Defaults to False.
  
      Returns:
          List[ChatPrediction]: List of chat predictions, each containing the assistant's generated response.
  
      Raises:
          AssertionError: If the last message in a dialog is not from the user.
          AssertionError: If the dialog roles are not in the required 'user', 'assistant', and optional 'system' order.
  
      Note:
          This method generates assistant responses for the provided conversational dialogs.
          It employs nucleus sampling to introduce controlled randomness in text generation.
          If logprobs is True, token log probabilities are computed for each generated token.
  
      """
      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 5 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