text: """Python wrappers around TensorFlow ops.
  
  This file is MACHINE GENERATED! Do not edit.
  """
  
  import collections as _collections
  import six as _six
  
  from tensorflow.python import pywrap_tensorflow as _pywrap_tensorflow
  from tensorflow.python.eager import context as _context
  from tensorflow.python.eager import core as _core
  from tensorflow.python.eager import execute as _execute
  from tensorflow.python.framework import dtypes as _dtypes
  from tensorflow.python.framework import errors as _errors
  from tensorflow.python.framework import tensor_shape as _tensor_shape
  
  from tensorflow.core.framework import op_def_pb2 as _op_def_pb2
  # Needed to trigger the call to _set_call_cpp_shape_fn.
  from tensorflow.python.framework import common_shapes as _common_shapes
  from tensorflow.python.framework import op_def_registry as _op_def_registry
  from tensorflow.python.framework import ops as _ops
  from tensorflow.python.framework import op_def_library as _op_def_library
  from tensorflow.python.util.deprecation import deprecated_endpoints
  from tensorflow.python.util.tf_export import tf_export
  
  
  @tf_export('ignite_dataset')
  def ignite_dataset(cache_name, host, port, local, part, page_size, schema, permutation, name=None):
    r"""IgniteDataset that allows to get data from Apache Ignite.
  
    Apache Ignite is a memory-centric distributed database, caching, and processing
    platform for transactional, analytical, and streaming workloads, delivering
    in-memory speeds at petabyte scale. This contrib package contains an
    integration between Apache Ignite and TensorFlow. The integration is based on
    tf.data from TensorFlow side and Binary Client Protocol from Apache Ignite side.
    It allows to use Apache Ignite as a datasource for neural network training,
    inference and all other computations supported by TensorFlow. Ignite Dataset
    is based on Apache Ignite Binary Client Protocol.
  
    Args:
      cache_name: A `Tensor` of type `string`. Ignite Cache Name.
      host: A `Tensor` of type `string`. Ignite Thin Client Host.
      port: A `Tensor` of type `int32`. Ignite Thin Client Port.
      local: A `Tensor` of type `bool`.
        Local flag that defines that data should be fetched from local host only.
      part: A `Tensor` of type `int32`. Partition data should be fetched from.
      page_size: A `Tensor` of type `int32`. Page size for Ignite Thin Client.
      schema: A `Tensor` of type `int32`.
        Internal structure that defines schema of cache objects.
      permutation: A `Tensor` of type `int32`.
        Internal structure that defines permutation of cache objects.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor` of type `variant`.
    """
    _ctx = _context._context
    if _ctx is None or not _ctx._eager_context.is_eager:
      _, _, _op = _op_def_lib._apply_op_helper(
          "IgniteDataset", cache_name=cache_name, host=host, port=port,
          local=local, part=part, page_size=page_size, schema=schema,
          permutation=permutation, name=name)
      _result = _op.outputs[:]
      _inputs_flat = _op.inputs
      _attrs = None
      _execute.record_gradient(
        "IgniteDataset", _inputs_flat, _attrs, _result, name)
      _result, = _result
      return _result
  
    else:
      try:
        _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
          _ctx._context_handle, _ctx._eager_context.device_name,
          "IgniteDataset", name, _ctx._post_execution_callbacks, cache_name,
          host, port, local, part, page_size, schema, permutation)
        return _result
      except _core._FallbackException:
        return ignite_dataset_eager_fallback(
            cache_name, host, port, local, part, page_size, schema, permutation,
            name=name, ctx=_ctx)
      except _core._NotOkStatusException as e:
        if name is not None:
          message = e.message + " name: " + name
        else:
          message = e.message
        _six.raise_from(_core._status_to_exception(e.code, message), None)
  
  
  def ignite_dataset_eager_fallback(cache_name, host, port, local, part, page_size, schema, permutation, name=None, ctx=None):
    r"""This is the slowpath function for Eager mode.
    This is for function ignite_dataset
    """
    _ctx = ctx if ctx else _context.context()
    cache_name = _ops.convert_to_tensor(cache_name, _dtypes.string)
    host = _ops.convert_to_tensor(host, _dtypes.string)
    port = _ops.convert_to_tensor(port, _dtypes.int32)
    local = _ops.convert_to_tensor(local, _dtypes.bool)
    part = _ops.convert_to_tensor(part, _dtypes.int32)
    page_size = _ops.convert_to_tensor(page_size, _dtypes.int32)
    schema = _ops.convert_to_tensor(schema, _dtypes.int32)
    permutation = _ops.convert_to_tensor(permutation, _dtypes.int32)
    _inputs_flat = [cache_name, host, port, local, part, page_size, schema, permutation]
    _attrs = None
    _result = _execute.execute(b"IgniteDataset", 1, inputs=_inputs_flat,
                               attrs=_attrs, ctx=_ctx, name=name)
    _execute.record_gradient(
        "IgniteDataset", _inputs_flat, _attrs, _result, name)
    _result, = _result
    return _result
  
  _ops.RegisterShape("IgniteDataset")(None)
  
  def _InitOpDefLibrary(op_list_proto_bytes):
    op_list = _op_def_pb2.OpList()
    op_list.ParseFromString(op_list_proto_bytes)
    _op_def_registry.register_op_list(op_list)
    op_def_lib = _op_def_library.OpDefLibrary()
    op_def_lib.add_op_list(op_list)
    return op_def_lib
  # op {
  #   name: "IgniteDataset"
  #   input_arg {
  #     name: "cache_name"
  #     type: DT_STRING
  #   }
  #   input_arg {
  #     name: "host"
  #     type: DT_STRING
  #   }
  #   input_arg {
  #     name: "port"
  #     type: DT_INT32
  #   }
  #   input_arg {
  #     name: "local"
  #     type: DT_BOOL
  #   }
  #   input_arg {
  #     name: "part"
  #     type: DT_INT32
  #   }
  #   input_arg {
  #     name: "page_size"
  #     type: DT_INT32
  #   }
  #   input_arg {
  #     name: "schema"
  #     type: DT_INT32
  #   }
  #   input_arg {
  #     name: "permutation"
  #     type: DT_INT32
  #   }
  #   output_arg {
  #     name: "handle"
  #     type: DT_VARIANT
  #   }
  #   is_stateful: true
  # }
  _op_def_lib = _InitOpDefLibrary(b"\n\203\001\n\rIgniteDataset\022\016\n\ncache_name\030\007\022\010\n\004host\030\007\022\010\n\004port\030\003\022\t\n\005local\030\n\022\010\n\004part\030\003\022\r\n\tpage_size\030\003\022\n\n\006schema\030\003\022\017\n\013permutation\030\003\032\n\n\006handle\030\025\210\001\001")
  