class_docstr: A `Dataset` consisting of the results from a SQL query.
  
  `SqlDataset` allows a user to read data from the result set of a SQL query.
  For example:
  
  ```python
  dataset = tf.data.experimental.SqlDataset("sqlite", "/foo/bar.sqlite3",
                                            "SELECT name, age FROM people",
                                            (tf.string, tf.int32))
  # Prints the rows of the result set of the above query.
  for element in dataset:
    print(element)
  ```
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docstr: Creates a `SqlDataset`.
  
  Args:
    driver_name: A 0-D `tf.string` tensor containing the database type.
      Currently, the only supported value is 'sqlite'.
    data_source_name: A 0-D `tf.string` tensor containing a connection string
      to connect to the database.
    query: A 0-D `tf.string` tensor containing the SQL query to execute.
    output_types: A tuple of `tf.DType` objects representing the types of the
      columns returned by `query`.
code: def __init__(
      self, driver_name, data_source_name, query, output_types
  ):
      """Creates a `SqlDataset`.
  
      Args:
        driver_name: A 0-D `tf.string` tensor containing the database type.
          Currently, the only supported value is 'sqlite'.
        data_source_name: A 0-D `tf.string` tensor containing a connection string
          to connect to the database.
        query: A 0-D `tf.string` tensor containing the SQL query to execute.
        output_types: A tuple of `tf.DType` objects representing the types of the
          columns returned by `query`.
      """
      self._driver_name = ops.convert_to_tensor(
          driver_name, dtype=dtypes.string, name="driver_name"
      )
      self._data_source_name = ops.convert_to_tensor(
          data_source_name, dtype=dtypes.string, name="data_source_name"
      )
      self._query = ops.convert_to_tensor(
          query, dtype=dtypes.string, name="query"
      )
      self._element_spec = nest.map_structure(
          lambda dtype: tensor_spec.TensorSpec([], dtype), output_types
      )
      variant_tensor = gen_experimental_dataset_ops.sql_dataset(
          self._driver_name,
          self._data_source_name,
          self._query,
          **self._flat_structure,
      )
      super(SqlDatasetV2, self).__init__(variant_tensor)
  
imports: import collections
  import csv
  import functools
  import gzip
  import numpy
  from tensorflow.python import tf2
  from tensorflow.python.compat import v2_compat
  from tensorflow.python.data.experimental.ops import error_ops
  from tensorflow.python.data.experimental.ops import parsing_ops
  from tensorflow.python.data.ops import dataset_ops
  from tensorflow.python.data.ops import map_op
  from tensorflow.python.data.ops import options
  from tensorflow.python.data.ops import readers
  from tensorflow.python.data.util import convert
  from tensorflow.python.data.util import nest
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor_spec
  from tensorflow.python.framework import tensor_util
  from tensorflow.python.lib.io import file_io
  from tensorflow.python.ops import gen_experimental_dataset_ops
  from tensorflow.python.ops import io_ops
  from tensorflow.python.platform import gfile
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 1 to create a new SqlDatasetV2 object from inherited base classes: dataset_ops with example: obj = SqlDatasetV2(driver_name, data_source_name, query, output_types)