class_docstr: The simple conditional residuals uncertainty model.
  For more details, see `~greykite.algo.uncertainty.conditional.conf_interval`.
  
  Attributes
  ----------
  uncertainty_dict : `dict` [`str`, any]
      The uncertainty model specification. It should have the following keys:
  
              "uncertainty_method": a string that is in
                  `~greykite.sklearn.uncertainty.uncertainty_methods.UncertaintyMethodEnum`.
              "params": a dictionary that includes any additional parameters needed by the uncertainty method.
  
  UNCERTAINTY_METHOD : `str`
      The name for the uncertainty method.
  REQUIRED_PARAMS : `list` [`str`]
      A list of required parameters for the method.
  coverage : `float`
      The coverage of the uncertainty intervals.
      Will be used to calculate the quantiles.
  time_col : `str`
      The column name for timestamps in ``train_df`` and ``fut_df``.
  value_col : `str`
      The column name for values in ``train_df`` and ``fut_df``.
  distribution_col : `str` or None
      The column name for the column used to fit the distribution in ``train_df`` and ``fut_df``.
  residual_col : `str` or None
      The column name for residuals in ``train_df``.
  conditional_cols : `list` [`str`]
      The conditional columns when calculating the standard errors of the residuals.
  offset_col : `str` or None
      The column name for the column used to offset predicted intervals.
  is_residual_based : `bool` or None
      Whether to fit the original values or residuals.
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docstr: Fits the uncertainty model.
  
  Parameters
  ----------
  train_df : `pandas.DataFrame`
      The data used to fit the uncertainty model.
      Must have the following columns:
  
          value_col: this is required
          time_col: if ``time_col`` is given.
          residual_col: if ``residual_col`` is given but ``PREDICT_COL`` does not exist.
          conditional_cols: if ``conditional_cols`` is given.
  
  Returns
  -------
  None
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code: def fit(self, train_df: pd.DataFrame):
      """Fits the uncertainty model.
  
      Parameters
      ----------
      train_df : `pandas.DataFrame`
          The data used to fit the uncertainty model.
          Must have the following columns:
  
              value_col: this is required
              time_col: if ``time_col`` is given.
              residual_col: if ``residual_col`` is given but ``PREDICT_COL`` does not exist.
              conditional_cols: if ``conditional_cols`` is given.
  
      Returns
      -------
      None
      """
      super().fit(train_df=train_df)
      self._check_input()
  
      self.uncertainty_model = conf_interval(
          df=self.train_df, **self.params
      )
  
file_path: greykite/greykite/sklearn/uncertainty/simple_conditional_residuals_model.py
imports: from typing import Dict
  from typing import List
  from typing import Optional
  import pandas
  from greykite.algo.forecast.silverkite.forecast_silverkite_helper import get_silverkite_uncertainty_dict
  from greykite.algo.uncertainty.conditional.conf_interval import conf_interval
  from greykite.algo.uncertainty.conditional.conf_interval import predict_ci
  from greykite.common import constants
  from greykite.common.features.timeseries_features import add_time_features_df
  from greykite.sklearn.uncertainty.base_uncertainty_model import BaseUncertaintyModel
  from greykite.sklearn.uncertainty.exceptions import UncertaintyError
label_desc: Use image node_id 3 for calling the SimpleConditionalResidualsModel obj's underlying member method code with example usage: obj.fit(train_df) without return types