code: def _transform(
      self,
      bundle: DataBundle,
      for_inference: bool,
      train_valid_T: Optional[Tuple[Any, Any]] = None,
  ) -> DataBundle:
      if not self._check_file(bundle.x_train):
          return bundle
      if not for_inference and not self.contain_labels:
          raise ValueError(
              "`contain_labels` should be True when not `for_inference`"
          )
      if train_valid_T is not None:
          train_T, valid_T = train_valid_T
      else:
          train_T = list(zip(*self._read(bundle.x_train)[1]))  # type: ignore
          if bundle.x_valid is None:
              valid_T = None
          else:
              valid_T = list(zip(*self._read(bundle.x_valid)[1]))
      if len(train_T) != len(self.all_header):
          if for_inference and len(train_T) + 1 == len(self.all_header):
              if self.is_local_rank_0:
                  print_info(
                      "labels are not detected and `for_inference` is set to True, "
                      "so `contain_labels` will be set to False"
                  )
              self.contain_labels = False
          else:
              raise ValueError(
                  f"number of columns ({len(train_T)}) does not match "
                  f"current number of columns ({len(self.all_header)})"
              )
      x_train, y_train = self._convert_data(train_T)
      if valid_T is None:
          x_valid = y_valid = None
      else:
          x_valid, y_valid = self._convert_data(valid_T)
      return DataBundle(x_train, y_train, x_valid, y_valid)
  
imports: import csv
  import numpy
  from typing import Any
  from typing import Dict
  from typing import List
  from typing import Tuple
  from typing import Union
  from typing import Optional
  from pathlib import Path
  from collections import Counter
  from dataclasses import dataclass
  from cftool.misc import print_info
  from cftool.misc import print_warning
  from cftool.misc import shallow_copy_dict
  from cftool.misc import ISerializable
  from cftool.misc import PureFromInfoMixin
  from cftool.array import is_float
  from ....schema import DataTypes
  from ....schema import DataBundle
  from ....schema import TDataBundleItem
  from ....schema import INoInitDataBlock
label_desc: Use image node_id 10 for calling the FileParserBlock obj's underlying member method code with example usage: obj._transform(bundle, for_inference, train_valid_T) and returns: DataBundle, bundle