class_docstr:     Client for the Anthropic models (https://arxiv.org/abs/2204.05862).
      They use their own tokenizer.
      Here are a list of bugs that we deal with in this client:
      - The prompt must contains anthropic.HUMAN_PROMPT ('
  
  Human:') and anthropic.AI_PROMPT ('
  
  Assistant:')
      - The completions is often too verbose, so we add the PROMPT_ANSWER_START to the prompt.
      TODO(#1521): Remove this when we have a better way to do prompt engineering.
      - The completions often start with a colon, space, or newline, so we remove it. This means
      that we need to query more tokens than necessary to ensure that the completion does not start
      with a colon, space, or newline. We query `max_tokens + ADDITIONAL_TOKENS` tokens and then
      remove the excess tokens at the end of the completion.
      TODO(#1512): Once we have a good way to post-process the completion, move this logic to the
      post-processing.
      - The API sometimes returns "Prompt must contain anthropic.AI_PROMPT". This is a bug related to the
      window_service that does not properly truncate some prompts.  It is caused by the suffix being truncated.
      - The API sometimes return Prompt length + max_tokens exceeds max (9192). This is something we do not
      handle yet (we have not limit on prompt length + max_tokens). TODO(#1520): Fix this.
      
code: def decode(self, request: DecodeRequest) -> DecodeRequestResult:
      # Method copied from HuggingFaceClient.
      # TODO: Unify this with HuggingFaceClient
      cache_key = asdict(request)
  
      try:
  
          def do_it():
              return {
                  "text": self.tokenizer.decode(
                      request.tokens,
                      clean_up_tokenization_spaces=request.clean_up_tokenization_spaces,
                  )
              }
  
          result, cached = self.cache.get(
              cache_key, wrap_request_time(do_it)
          )
      except Exception as e:
          error: str = f"HuggingFace error: {e}"
          return DecodeRequestResult(
              success=False, cached=False, error=error, text=""
          )
  
      return DecodeRequestResult(
          success=True,
          cached=cached,
          text=result["text"],
          request_time=result["request_time"],
      )
  
imports: from typing import Any, Dict, List, Optional
  import json
  import requests
  import time
  import urllib.parse
  import websocket
  import anthropic
  from helm.common.cache import Cache, CacheConfig
  from helm.common.hierarchical_logger import htrack_block, hlog
  from helm.common.request import EMBEDDING_UNAVAILABLE_REQUEST_RESULT, Request, RequestResult, Sequence, Token, ErrorFlags
  from helm.common.tokenization_request import TokenizationRequest, TokenizationRequestResult, DecodeRequest, DecodeRequestResult, TokenizationToken
  from .client import Client, wrap_request_time, truncate_sequence
  from transformers import PreTrainedTokenizerBase, PreTrainedTokenizerFast
  from dataclasses import asdict
label_desc: Use image node_id 6 for calling the AnthropicClient obj's underlying member method code with example usage: obj.decode(request) and returns: DecodeRequestResult, dict, DecodeRequestResult