class_docstr: GRPCStreamIODataset
docstr: Create a GRPC Reader.
  
  Args:
      endpoint: A `tf.string` tensor containing one or more endpoints.
docstr_tok: ["Create","a","GRPC","Reader",".","Args",":","endpoint",":","A","`","tf.string","`","tensor","containing","one","or","more","endpoints","."]
code: def __init__(self, endpoint, shape, dtype):
      """Create a GRPC Reader.
  
      Args:
          endpoint: A `tf.string` tensor containing one or more endpoints.
      """
      with tf.name_scope("GRPCStreamIODataset"):
          shape = tf.cast(shape, tf.int64)
  
          resource = core_ops.io_grpc_readable_init(endpoint)
  
          self._resource = resource
          self._shape = tf.cast(shape, tf.int64)
          self._dtype = dtype
  
          step = 1
          indices_start = tf.data.Dataset.range(0, shape[0], step)
          indices_stop = indices_start.skip(1).concatenate(
              tf.data.Dataset.from_tensor_slices([shape[0]])
          )
          dataset = tf.data.Dataset.zip((indices_start, indices_stop))
  
          def f(start, stop):
              shape = tf.concat(
                  [
                      tf.convert_to_tensor([stop - start], tf.int64),
                      self._shape[1:],
                  ],
                  axis=0,
              )
              return core_ops.io_grpc_readable_read(
                  self._resource,
                  start=start,
                  shape=shape,
                  dtype=self._dtype,
              )
  
          dataset = dataset.map(f)
          dataset = dataset.unbatch()
  
          self._dataset = dataset
          super().__init__(self._dataset._variant_tensor)
  
file_path: io/tensorflow_io/python/experimental/grpc_dataset_ops.py
imports: import tensorflow
  from tensorflow_io.python.ops import core_ops
label_desc: Use image node_id 1 to create a new GRPCStreamIODataset object from inherited base classes: tf.data with example: obj = GRPCStreamIODataset(endpoint, shape, dtype)