class_docstr: Iterator for a single DistributedDatasetV1 instance.
class_docstr_tok: ["Iterator","for","a","single","DistributedDatasetV1","instance","."]
docstr: Make appropriate iterator on the dataset.
code: def _make_iterator(self):
      """Make appropriate iterator on the dataset."""
      with ops.device(self._worker):
          if self._options is not None:
              self._iterator = multi_device_iterator_ops.MultiDeviceIterator(
                  self._dataset,
                  self._devices,
                  max_buffer_size=self._options.experimental_per_replica_buffer_size,
                  prefetch_buffer_size=self._options.experimental_per_replica_buffer_size,
              )
          else:
              self._iterator = (
                  multi_device_iterator_ops.MultiDeviceIterator(
                      self._dataset,
                      self._devices,
                  )
              )
  
imports: from tensorflow.python.data.experimental.ops import cardinality
  from tensorflow.python.data.ops import dataset_ops
  from tensorflow.python.data.ops import multi_device_iterator_ops
  from tensorflow.python.data.ops import optional_ops
  from tensorflow.python.distribute import input_lib
  from tensorflow.python.eager import context
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import control_flow_ops
  from tensorflow.python.types import data
  from tensorflow.python.util.deprecation import deprecated
label_desc: Use image node_id 1 for calling the _SingleWorkerDatasetIterator obj's underlying member method code with example usage: obj._make_iterator() without return types