class_docstr: Automatically count tokens based on the organization.
docstr: Return a `TokenCostEstimator` based on the organization.
docstr_tok: ["Return","a","`","TokenCostEstimator","`","based","on","the","organization","."]
code: def _get_estimator(self, organization: str) -> TokenCostEstimator:
      """Return a `TokenCostEstimator` based on the organization."""
      token_cost_estimator = self._token_cost_estimators.get(
          organization
      )
  
      if token_cost_estimator is None:
          if organization == "openai":
              token_cost_estimator = OpenAITokenCostEstimator()
          elif organization == "ai21":
              token_cost_estimator = AI21TokenCostEstimator()
          elif organization == "cohere":
              token_cost_estimator = CohereTokenCostEstimator()
          elif organization == "gooseai":
              token_cost_estimator = GooseAITokenCostEstimator()
          else:
              token_cost_estimator = FreeTokenCostEstimator()
          self._token_cost_estimators[
              organization
          ] = token_cost_estimator
  
      return token_cost_estimator
  
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 2 for calling the AutoTokenCostEstimator obj's underlying member method code with example usage: obj._get_estimator(organization) and returns: token_cost_estimator