docstr: Catches errors when an iterator runs out of data.
code: def catch_stop_iteration(self):
      """Catches errors when an iterator runs out of data."""
      try:
          yield
          self.tf_sync()
      except (StopIteration, tf.errors.OutOfRangeError):
          if self._num_batches is None:
              self._num_batches = self._steps_seen
          warnings.warn(
              "Your input ran out of data; interrupting training. "
              "Make sure that your dataset or generator can generate "
              "at least `steps_per_epoch * epochs` batches. "
              "You may need to use the `.repeat()` "
              "function when building your dataset.",
              stacklevel=2,
          )
          self._current_iterator = None
          self.data_adapter.on_epoch_end()
  
imports: import contextlib
  import warnings
  import numpy
  import tensorflow
  import tree
  from packaging.version import Version
  from tensorflow.python.eager import context
  from keras import callbacks
  from keras import metrics
  from keras import optimizers
  from keras.trainers import trainer
  from keras.trainers.data_adapters import data_adapter_utils
  from keras.trainers.epoch_iterator import EpochIterator
  from keras.utils import traceback_utils
label_desc: Use image node_id 4 for calling the TFEpochIterator obj's underlying member method code with example usage: obj.catch_stop_iteration() without return types