class_docstr: Class for building prediction intervals based on Fedot functionality.
  
  Args:
      model: a fitted Fedot class object for times series forecasting task
      horizon: horizon for ts forecasting
      nominal_error: nominal error for prediction intervals task
      method: general method for creating forecasts. Possible options:
  
           - 'last_generation_ql' -> a number of pipelines from the last generation is fitted using
              quantile loss/pinball loss metric.
           - 'best_pipelines_quantiles' -> prediction intervals are computed based on predictions of all
              pipelines in last generation
           - 'mutation_of_best_pipeline' -> prediction intervals are built relying on mutations of the final
              pipeline
  
      params: params for building prediction intervals.
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docstr: Method to get quantiles based on predictions of final pipeline over train_data.
  
  Args:
      train_input: train data.
  
  Returns:
      dictionary consisting of upper and low quantiles computed for model forecast residuals over train ts.
docstr_tok: ["Method","to","get","quantiles","based","on","predictions","of","final","pipeline","over","train_data",".","Args",":","train_input",":","train","data",".","Returns",":","dictionary","consisting","of","upper","and","low","quantiles","computed","for","model","forecast","residuals","over","train","ts","."]
code: def get_base_quantiles(self, train_input: InputData):
      """Method to get quantiles based on predictions of final pipeline over train_data.
  
      Args:
          train_input: train data.
  
      Returns:
          dictionary consisting of upper and low quantiles computed for model forecast residuals over train ts.
      """
      base_quantiles = get_base_quantiles(
          train_input,
          pipeline=self.best_pipeline,
          nominal_error=self.nominal_error,
      )
  
      self.base_quantiles = {
          "up": self.model_forecast + base_quantiles["up"],
          "low": self.model_forecast + base_quantiles["low"],
      }
      self.base_quantiles_are_computed = True
  
      return self.base_quantiles
  
file_path: FEDOT/fedot/core/pipelines/prediction_intervals/main.py
imports: from functools import partial
  from typing import Optional
  import numpy
  from golem.core.log import default_log, Log
  from fedot import Fedot
  from fedot.core.data.data import InputData
  from fedot.core.pipelines.prediction_intervals.params import PredictionIntervalsParams
  from fedot.core.pipelines.prediction_intervals.solvers.best_pipelines_quantiles import solver_best_pipelines_quantiles
  from fedot.core.pipelines.prediction_intervals.solvers.last_generation_quantile_loss import solver_last_generation_ql
  from fedot.core.pipelines.prediction_intervals.solvers.mutation_of_best_pipeline import solver_mutation_of_best_pipeline
  from fedot.core.pipelines.prediction_intervals.utils import compute_prediction_intervals, get_base_quantiles, check_init_params, get_last_generations
  from fedot.core.pipelines.prediction_intervals.visualization import plot_prediction_intervals, _plot_prediction_intervals
label_desc: Use image node_id 4 for calling the PredictionIntervals obj's underlying member method code with example usage: obj.get_base_quantiles(train_input) and returns: self