code: def forecast(self, post_data):
      # This is a separate function from predict since we apply
      # transformations to an entire time series. That enables us
      # to only send the transformation information from UI to backend
      # rather than having to send the entire time series across.
      try:
          filters = post_data[0]
          composite_filters = post_data[1]
          transformation = post_data[2]
          filtered_data_df = self._analysis.get_filtered_test_data(
              filters=filters,
              composite_filters=composite_filters,
              include_original_columns_only=True,
          )
  
          transformation_func = None
          # transforming with pandas
          if len(transformation) > 0:
              op, feature, value = transformation
              if op == "add":
  
                  def add(x):
                      return x + float(value)
  
                  transformation_func = add
              elif op == "subtract":
  
                  def subtract(x):
                      return x - float(value)
  
                  transformation_func = subtract
              elif op == "multiply":
  
                  def multiply(x):
                      return x * float(value)
  
                  transformation_func = multiply
              elif op == "divide":
  
                  def divide(x):
                      return x / float(value)
  
                  transformation_func = divide
              elif op == "change":
  
                  def change(x):
                      return float(value)
  
                  transformation_func = change
              else:
                  raise ValueError(
                      f"An invalid transformation operation {op} "
                      "was provided."
                  )
  
              filtered_data_df[feature] = filtered_data_df[feature].map(
                  transformation_func
              )
  
          predictions = convert_to_list(
              self._analysis.model.forecast(filtered_data_df),
              EXP_VIZ_ERR_MSG,
          )
          # forecast should return a flat list of predictions
          if all([len(p) == 1 for p in predictions]):
              predictions = [p[0] for p in predictions]
          return {WidgetRequestResponseConstants.data: predictions}
      except Exception as e:
          print(e)
          traceback.print_exc()
          e_str = _format_exception(e)
          return {
              WidgetRequestResponseConstants.error: "Failed to generate forecast for time series, "
              "inner error: {}".format(e_str),
              WidgetRequestResponseConstants.data: [],
          }
  
imports: import traceback
  from typing import List, Optional
  import numpy
  import pandas
  from erroranalysis._internal.constants import display_name_to_metric
  from raiutils.cohort import Cohort, CohortFilter, CohortFilterMethods
  from raiutils.data_processing import convert_to_list, serialize_json_safe
  from raiutils.exceptions import UserConfigValidationException
  from raiutils.models import ModelTask, is_classifier
  from raiwidgets.constants import ErrorMessages
  from raiwidgets.error_handling import _format_exception
  from raiwidgets.interfaces import WidgetRequestResponseConstants
  from responsibleai import RAIInsights
  from responsibleai._internal.constants import ManagerNames
label_desc: Use image node_id 14 for calling the ResponsibleAIDashboardInput obj's underlying member method code with example usage: obj.forecast(post_data) and returns: dict, dict, unknown, unknown, unknown, unknown, float