code: def calculate_yhats_distributions(explainer):
      data_yhat = explainer.predict(explainer.data)
  
      return pd.DataFrame(
          {
              "variable_name": "all_data",
              "variable": "all data",
              "id": np.arange(explainer.data.shape[0]),
              "prediction": data_yhat,
              "label": explainer.label,
          }
      )
  
file_path: DALEX/python/dalex/dalex/predict_explanations/_shap/utils.py
imports: import multiprocessing
  from copy import deepcopy
  from numpy.random import SeedSequence, default_rng
  import numpy
  import pandas
label_desc: Use image node_id 3 for calling a global function with example usage: calculate_yhats_distributions(explainer) and returns: pd