code: def preprocess_data_by_treatment(
      data: pd.DataFrame,
      treatment_name: List[str],
      unobserved_confounder_values: Optional[np.ndarray],
      bucket_size_scale_factor: float,
      chosen_variables: List[str],
  ):
      """
      This function groups data based on the data type of the treatment.
  
      Expected variable types supported for the treatment:
  
      * bool
      * pd.categorical
      * float
      * int
  
      :returns: ``pandas.core.groupby.generic.DataFrameGroupBy``
      """
      assert (
          len(treatment_name) == 1
      ), "At present, DoWhy supports a simgle treatment variable"
  
      if unobserved_confounder_values is not None:
          data["simulated"] = unobserved_confounder_values
          chosen_variables.append("simulated")
  
      treatment_variable_name = treatment_name[
          0
      ]  # As we only have a single treatment
      variable_type = data[treatment_variable_name].dtypes
  
      if bool == variable_type:
          groups = data.groupby(treatment_variable_name)
          return groups
      # We use string arguments to account for both 32 and 64 bit varaibles
      elif "float" in variable_type.name or "int" in variable_type.name:
          # action for continuous variables
          data = data
          std_dev = data[treatment_variable_name].std()
          num_bins = (data.max() - data.min()) / (
              bucket_size_scale_factor * std_dev
          )
          data["bins"] = pd.cut(data[treatment_variable_name], num_bins)
          groups = data.groupby("bins")
          data.drop("bins", axis=1, inplace=True)
          return groups
  
      elif "categorical" in variable_type.name:
          # Action for categorical variables
          groups = data.groupby(treatment_variable_name)
          groups = data.groupby("bins")
          return groups
      else:
          raise ValueError(
              "Passed {}. Expected bool, float, int or categorical.".format(
                  variable_type.name
              )
          )
  
imports: import copy
  import logging
  from collections import OrderedDict, namedtuple
  from typing import Callable, List, Optional, Union
  import numpy
  import pandas
  from sklearn.ensemble import RandomForestRegressor
  from sklearn.linear_model import LinearRegression
  from sklearn.neighbors import KNeighborsRegressor
  from sklearn.neural_network import MLPRegressor
  from sklearn.svm import SVR
  from tqdm.auto import tqdm
  from dowhy.causal_estimator import CausalEstimate
  from dowhy.causal_estimators.econml import Econml
  from dowhy.causal_identifier.identified_estimand import IdentifiedEstimand
  from dowhy.causal_refuter import CausalRefutation, CausalRefuter, choose_variables, test_significance
label_desc: Use image node_id 4 for calling a global function with example usage: preprocess_data_by_treatment(data, treatment_name, unobserved_confounder_values, bucket_size_scale_factor, chosen_variables) and returns: groups, groups, groups