code: def _transform(
      self,
      bundle: DataBundle,
      for_inference: bool,
      x_train_nan_mask: Optional[np.ndarray] = None,
      y_train_nan_mask: Optional[np.ndarray] = None,
      x_valid_nan_mask: Optional[np.ndarray] = None,
      y_valid_nan_mask: Optional[np.ndarray] = None,
  ) -> DataBundle:
      if not isinstance(bundle.x_train, np.ndarray):
          raise ValueError(
              "`NanHandlerBlock` should be used on numpy features."
          )
      if bundle.y_train is not None and not isinstance(
          bundle.y_train, np.ndarray
      ):
          raise ValueError(
              "`NanHandlerBlock` should be used on numpy labels."
          )
      if x_train_nan_mask is None:
          x_train_nan_mask = np.isnan(bundle.x_train)
      if y_train_nan_mask is None and bundle.y_train is not None:
          y_train_nan_mask = np.isnan(bundle.y_train)
      if x_valid_nan_mask is None and bundle.x_valid is not None:
          x_valid_nan_mask = np.isnan(bundle.x_valid)
      if y_valid_nan_mask is None and bundle.y_valid is not None:
          y_valid_nan_mask = np.isnan(bundle.y_valid)
      # drop nan
      if (
          not for_inference
          and self.drop_strategy != NanDropStrategy.NONE
      ):
          only_drop_y = self.drop_strategy == NanDropStrategy.DROP_Y
          bundle.x_train, bundle.y_train = drop_nan(
              bundle.x_train,
              bundle.y_train,
              x_train_nan_mask,
              y_train_nan_mask,
              only_drop_y=only_drop_y,
          )
          if bundle.x_valid is not None:
              bundle.x_valid, bundle.y_valid = drop_nan(
                  bundle.x_valid,  # type: ignore
                  bundle.y_valid,  # type: ignore
                  x_valid_nan_mask,  # type: ignore
                  y_valid_nan_mask,  # type: ignore
                  only_drop_y=only_drop_y,
              )
      # replace nan
      for idx in range(bundle.x_train.shape[-1]):
          cn = self._get_column_name(idx, False)
          replacement = self.replacements.get(str(idx))
          replace_nan(
              bundle.x_train, x_train_nan_mask, idx, cn, replacement
          )
          if bundle.x_valid is not None:
              replace_nan(bundle.x_valid, x_valid_nan_mask, idx, cn, replacement)  # type: ignore
      if bundle.y_train is not None:
          for idx in range(bundle.y_train.shape[-1]):
              cn = self._get_column_name(idx, True)
              replacement = self.label_replacements.get(str(idx))
              replace_nan(bundle.y_train, y_train_nan_mask, idx, cn, replacement)  # type: ignore
              if bundle.y_valid is not None:
                  replace_nan(bundle.y_valid, y_valid_nan_mask, idx, cn, replacement)  # type: ignore
      return bundle
  
imports: import numpy
  from enum import Enum
  from typing import Any
  from typing import Dict
  from typing import Optional
  from dataclasses import dataclass
  from .file import TArrayPair
  from .file import FileParserBlock
  from ....schema import DataBundle
  from ....schema import INoInitDataBlock
label_desc: Use image node_id 7 for calling the NanHandlerBlock obj's underlying member method code with example usage: obj._transform(bundle, for_inference, x_train_nan_mask, y_train_nan_mask, x_valid_nan_mask, y_valid_nan_mask) and returns: bundle