code: def completions_to_model(cls, payload, config):
      payload = super().completions_to_model(payload, config)
  
      if "\n\nHuman:" not in payload.get("stop_sequences", []):
          payload.setdefault("stop_sequences", []).append("\n\nHuman:")
  
      payload["max_tokens_to_sample"] = min(
          payload.get(
              "max_tokens_to_sample",
              MLFLOW_AI_GATEWAY_ANTHROPIC_DEFAULT_MAX_TOKENS,
          ),
          AWS_BEDROCK_ANTHROPIC_MAXIMUM_MAX_TOKENS,
      )
      return payload
  
imports: import json
  import time
  from enum import Enum
  import boto3
  import botocore.config
  import botocore.exceptions
  from fastapi import HTTPException
  from fastapi.encoders import jsonable_encoder
  from mlflow.gateway.config import AWSBedrockConfig, AWSIdAndKey, AWSRole, RouteConfig
  from mlflow.gateway.constants import MLFLOW_AI_GATEWAY_ANTHROPIC_DEFAULT_MAX_TOKENS
  from mlflow.gateway.exceptions import AIGatewayConfigException
  from mlflow.gateway.providers.anthropic import AnthropicAdapter
  from mlflow.gateway.providers.base import BaseProvider, ProviderAdapter
  from mlflow.gateway.providers.cohere import CohereAdapter
  from mlflow.gateway.providers.utils import rename_payload_keys
  from mlflow.gateway.schemas import chat, completions, embeddings
label_desc: Use image node_id 1 for calling the AWSBedrockAnthropicAdapter obj's underlying member method code with example usage: obj.completions_to_model(cls, payload, config) and returns: payload