code: def cate_mean_by_bin_meta_evaluator(
      test_data: pd.DataFrame,
      group_column: str,
      control_group_name: str,
      bin_column: str,
      n_bins: int,
      allow_dropped_bins: bool = False,
      inner_evaluator: UncurriedEvalFnType = r2_evaluator,
      eval_name: str = None,
      prediction_column: str = "prediction",
      target_column: str = "target",
  ) -> EvalReturnType:
      """
      Evaluates the predictions of a causal model that outputs treatment outcomes w.r.t. its capabilities to predict the
      CATE.
  
      Due to the fundamental lack of counterfactual data, the CATEs are computed for bins of a given column. This function
      then applies a fklearn-like evaluator on top of the aggregated dataframe.
  
      Parameters
      ----------
      test_data : DataFrame
          A Pandas' DataFrame with `group_column` as a column.
  
      group_column : str
          The name of the column that tells whether rows belong to the test or control group.
  
      control_group_name : str
          The name of the control group.
  
      bin_column : str
          The name of the column from which the quantiles will be created.
  
      n_bins : str
          The number of bins to be created.
  
      allow_dropped_bins : bool, optional (default=False)
          Whether to allow the function to drop duplicated quantiles.
  
      inner_evaluator : UncurriedEvalFnType, optional (default=r2_evaluator)
          An instance of a fklearn-like evaluator, which will be applied to the .
  
      eval_name : str, optional (default=None)
          The name of the evaluator as it will appear in the logs.
  
      prediction_column : str, optional (default=None)
          The name of the column containing the predictions from the model being evaluated.
  
      target_column : str, optional (default=None)
          The name of the column containing the actual outcomes of the treatment.
  
      Returns
      ----------
      log: dict
          A log-like dictionary with the evaluation by `inner_evaluator`
      """
      try:
          gb = cate_mean_by_bin(
              test_data,
              group_column,
              control_group_name,
              bin_column,
              n_bins,
              allow_dropped_bins,
              prediction_column,
              target_column,
          )
      except ValueError:
          raise ValueError(
              "can't create {} bins for column '{}'. use 'allow_dropped_bins=True' to drop duplicated bins".format(
                  n_bins, bin_column
              )
          )
  
      if eval_name is None:
          eval_name = (
              "cate_mean_by_bin_"
              + bin_column
              + "[{}q]".format(n_bins)
              + "__"
              + inner_evaluator.__name__
          )
  
      return inner_evaluator(
          test_data=gb,
          prediction_column=prediction_column,
          target_column=target_column,
          eval_name=eval_name,
      )
  
imports: import pandas
  from toolz import curry
  from fklearn.types import EvalReturnType, UncurriedEvalFnType
  from fklearn.validation.evaluators import r2_evaluator
label_desc: Use image node_id 3 for calling a global function with example usage: cate_mean_by_bin_meta_evaluator(test_data, group_column, control_group_name, bin_column, n_bins, allow_dropped_bins, inner_evaluator, eval_name, prediction_column, target_column) and returns: inner_evaluator