code: def bundle_weights(self, X, w, X_val, w_val):
      if w is not None:
          X[self.sample_weight] = w
          if X_val is not None:
              if w_val is not None:
                  X_val[self.sample_weight] = w_val
              elif not self.weight_evaluation:
                  nan_vals = np.empty((len(X_val),))
                  nan_vals[:] = np.nan
                  X_val[self.sample_weight] = nan_vals
              else:
                  raise ValueError(
                      f"sample_weight column '{self.sample_weight}' cannot be missing from X_val if weight_evaluation=True"
                  )
      return X, X_val
  
imports: import copy
  import logging
  import math
  import time
  import numpy
  import pandas
  from pandas import DataFrame
  from autogluon.common.utils.log_utils import convert_time_in_s_to_log_friendly
  from autogluon.common.utils.system_info import get_ag_system_info
  from autogluon.core.constants import AUTO_WEIGHT, BALANCE_WEIGHT, BINARY, MULTICLASS, QUANTILE, REGRESSION
  from autogluon.core.data import LabelCleaner
  from autogluon.core.data.cleaner import Cleaner
  from autogluon.core.utils.time import sample_df_for_time_func, time_func
  from autogluon.core.utils.utils import augment_rare_classes, extract_column
  from ..trainer import AutoTrainer
  from .abstract_learner import AbstractTabularLearner
label_desc: Use image node_id 5 for calling the DefaultLearner obj's underlying member method code with example usage: obj.bundle_weights(X, w, X_val, w_val) and returns: X,  X_val