code: def _print_train_info(
      num_samples_or_steps, val_samples_or_steps, is_dataset
  ):
      increment = "steps" if is_dataset else "samples"
      msg = "Train on {0} {increment}".format(
          num_samples_or_steps, increment=increment
      )
      if val_samples_or_steps:
          msg += ", validate on {0} {increment}".format(
              val_samples_or_steps, increment=increment
          )
      print(msg)
  
imports: import functools
  import numpy
  from tensorflow.python.data.ops import iterator_ops
  from tensorflow.python.eager import context
  from tensorflow.python.framework import errors
  from tensorflow.python.keras import backend
  from tensorflow.python.keras import callbacks
  from tensorflow.python.keras.distribute import distributed_training_utils_v1
  from tensorflow.python.keras.engine import training_utils_v1
  from tensorflow.python.keras.utils.generic_utils import make_batches
  from tensorflow.python.keras.utils.generic_utils import slice_arrays
  from tensorflow.python.keras.utils.mode_keys import ModeKeys
  from tensorflow.python.platform import tf_logging
  from tensorflow.python.types import data
  from tensorflow.python.util import nest
label_desc: Use image node_id 3 for calling a global function with example usage: _print_train_info(num_samples_or_steps, val_samples_or_steps, is_dataset) without return types