class_docstr: Distributed dataset that supports prefetching to multiple devices.
class_docstr_tok: ["Distributed","dataset","that","supports","prefetching","to","multiple","devices","."]
code: def __init__(
      self,
      dataset,
      input_workers,
      strategy,
      num_replicas_in_sync=None,
      input_context=None,
      options=None,
  ):
      self._input_workers = input_workers
      super(DistributedDatasetV1, self).__init__(
          input_workers,
          strategy,
          dataset,
          num_replicas_in_sync=num_replicas_in_sync,
          input_context=input_context,
          options=options,
      )
  
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 to create a new DistributedDatasetV1 object from inherited base classes: input_lib with example: obj = DistributedDatasetV1(dataset, input_workers, strategy, num_replicas_in_sync, input_context, options)