class_docstr: Utility class to facilitate API queries and state management of
  session data.
docstr: Prepares the next batch of data based on the request url and
  the counter index.
  
  Args:
      raw_value: A serialized JSON record in tf.string format.
      columns: list of columns to prepare the structured data.
      dtypes: tf.dtypes of the columns.
  Returns:
      Structured data with columns as keys and the corresponding tensors as values.
docstr_tok: ["Prepares","the","next","batch","of","data","based","on","the","request","url","and","the","counter","index",".","Args",":","raw_value",":","A","serialized","JSON","record","in","tf.string","format",".","columns",":","list","of","columns","to","prepare","the","structured","data",".","dtypes",":","tf.dtypes","of","the","columns",".","Returns",":","Structured","data","with","columns","as","keys","and","the","corresponding","tensors","as","values","."]
code: def parse_json(self, raw_item, columns, dtypes):
      """Prepares the next batch of data based on the request url and
      the counter index.
  
      Args:
          raw_value: A serialized JSON record in tf.string format.
          columns: list of columns to prepare the structured data.
          dtypes: tf.dtypes of the columns.
      Returns:
          Structured data with columns as keys and the corresponding tensors as values.
      """
      specs = {}
      for column, dtype in zip(columns, dtypes):
          specs[column.decode("utf-8")] = tf.TensorSpec(
              tf.TensorShape([]), dtype
          )
      parsed_item = serialization_ops.decode_json(
          data=raw_item, specs=specs
      )
      return parsed_item
  
file_path: io/tensorflow_io/python/experimental/elasticsearch_dataset_ops.py
imports: from urllib.parse import urlparse
  import tensorflow
  from tensorflow_io.python.ops import core_ops
  from tensorflow_io.python.experimental import serialization_ops
label_desc: Use image node_id 6 for calling the _ElasticsearchHandler obj's underlying member method code with example usage: obj.parse_json(raw_item, columns, dtypes) and returns: parsed_item