docstr: Overwrite the current datatypes for certain objectives.
  Predictions obtained from lightgbm are stored as these
  datatypes.
  
  Parameters
  ----------
  objective_pred_dtypes: dict
      A dict of {objective: np.datatype} pairs.
      Datatype must be the string datatype name, i.e. "float32"
      How prediction data types will be cast. Casting to a smaller bit
      value can be beneficial when compiling candidates. Depending on
      the data, smaller bit rates tend to result in imputations of
      sufficient quality, while taking up much less space.
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code: def set_objective_pred_dtypes(
      self, objective_pred_dtypes: Dict[str, str]
  ):
      """
      Overwrite the current datatypes for certain objectives.
      Predictions obtained from lightgbm are stored as these
      datatypes.
  
      Parameters
      ----------
      objective_pred_dtypes: dict
          A dict of {objective: np.datatype} pairs.
          Datatype must be the string datatype name, i.e. "float32"
          How prediction data types will be cast. Casting to a smaller bit
          value can be beneficial when compiling candidates. Depending on
          the data, smaller bit rates tend to result in imputations of
          sufficient quality, while taking up much less space.
  
      """
      self.objective_pred_dtypes.update(objective_pred_dtypes)
  
file_path: miceforest/miceforest/MeanMatchScheme.py
imports: from .compat import pd_DataFrame
  import inspect
  from copy import deepcopy
  from typing import Callable, Union, Dict, Set
  from numpy import dtype
label_desc: Use image node_id 9 for calling the MeanMatchScheme obj's underlying member method code with example usage: obj.set_objective_pred_dtypes(objective_pred_dtypes) without return types