class_docstr: Automatically count tokens based on the organization.
docstr: Estimate the number of tokens for a given request based on the organization.
docstr_tok: ["Estimate","the","number","of","tokens","for","a","given","request","based","on","the","organization","."]
code: def estimate_tokens(
      self, request: Request, metric_service: MetricService
  ) -> int:
      """
      Estimate the number of tokens for a given request based on the organization.
      """
      token_cost_estimator: TokenCostEstimator = self._get_estimator(
          request.model_organization
      )
      return token_cost_estimator.estimate_tokens(
          request, metric_service
      )
  
file_path: H2O/h2o_hf/helm/src/helm/benchmark/metrics/tokens/auto_token_cost_estimator.py
imports: from typing import Dict
  from helm.benchmark.metrics.metric_service import MetricService
  from helm.common.request import Request
  from .ai21_token_cost_estimator import AI21TokenCostEstimator
  from .cohere_token_cost_estimator import CohereTokenCostEstimator
  from .free_token_cost_estimator import FreeTokenCostEstimator
  from .gooseai_token_cost_estimator import GooseAITokenCostEstimator
  from .openai_token_cost_estimator import OpenAITokenCostEstimator
  from .token_cost_estimator import TokenCostEstimator
label_desc: Use image node_id 3 for calling the AutoTokenCostEstimator obj's underlying member method code with example usage: obj.estimate_tokens(request, metric_service) and returns: token_cost_estimator