code: def calculate_prediction(self, df):
      if self.param["model_name"] == "xgboost":
          import xgboost
  
          if xgboost.__version__ in [
              "1.1.0",
              "1.1.1",
              "1.1.0rc2",
              "1.1.0rc1",
          ]:
              print(
                  "Current Xgboost version is not supported. Please install Xgboost using 'pip install xgboost==1.0.2'"
              )
              return False
          prediction_col = self.param["model"].predict(
              xgboost.DMatrix(df)
          )
  
      elif self.param["model_name"] == "catboost":
          prediction_col = self.param["model"].predict(df.to_numpy())
  
      elif self.param["model_name"] == "h2o":
          df = h2o.H2OFrame(df)
          prediction_col = self.param["model"].predict(df)
  
      else:
          prediction_col = self.param["model"].predict(df.to_numpy())
  
      # is classification?
      is_classification = self.param["is_classification"]
  
      # prediction col
      df[self.param["y_variable_predict"]] = prediction_col
  
      return df
  
imports: from imports import *
  from plotly_graphs import *
  from protodash import *
  from insights import *
  from plotly_css import *
  import pandasql
  import string
  import random
  import os
  from apps import global_explanation, local_explanation, distribution, feature_interaction, cohort
  from app import app
  from what_if import *
  from calculate_shap import *
  from analytics import Analytics
  from cohort_analysis import *
label_desc: Use image node_id 17 for calling the dashboard obj's underlying member method code with example usage: obj.calculate_prediction(df) and returns: df, False