@_dispatch.add_dispatch_list
@tf_export('ignite_dataset')
def ignite_dataset(cache_name, host, port, local, part, page_size, schema, permutation, name=None):
    'IgniteDataset that allows to get data from Apache Ignite.\n\n  Apache Ignite is a memory-centric distributed database, caching, and processing\n  platform for transactional, analytical, and streaming workloads, delivering\n  in-memory speeds at petabyte scale. This contrib package contains an\n  integration between Apache Ignite and TensorFlow. The integration is based on\n  tf.data from TensorFlow side and Binary Client Protocol from Apache Ignite side.\n  It allows to use Apache Ignite as a datasource for neural network training,\n  inference and all other computations supported by TensorFlow. Ignite Dataset\n  is based on Apache Ignite Binary Client Protocol.\n\n  Args:\n    cache_name: A `Tensor` of type `string`. Ignite Cache Name.\n    host: A `Tensor` of type `string`. Ignite Thin Client Host.\n    port: A `Tensor` of type `int32`. Ignite Thin Client Port.\n    local: A `Tensor` of type `bool`.\n      Local flag that defines that data should be fetched from local host only.\n    part: A `Tensor` of type `int32`. Partition data should be fetched from.\n    page_size: A `Tensor` of type `int32`. Page size for Ignite Thin Client.\n    schema: A `Tensor` of type `int32`.\n      Internal structure that defines schema of cache objects.\n    permutation: A `Tensor` of type `int32`.\n      Internal structure that defines permutation of cache objects.\n    name: A name for the operation (optional).\n\n  Returns:\n    A `Tensor` of type `variant`.\n  '
    _ctx = (_context._context or _context.context())
    if ((_ctx is not None) and _ctx._thread_local_data.is_eager):
        try:
            _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(_ctx._context_handle, _ctx._thread_local_data.device_name, 'IgniteDataset', name, _ctx.post_execution_callbacks, cache_name, host, port, local, part, page_size, schema, permutation)
            return _result
        except _core._FallbackException:
            try:
                return ignite_dataset_eager_fallback(cache_name, host, port, local, part, page_size, schema, permutation, name=name, ctx=_ctx)
            except _core._SymbolicException:
                pass
            except (TypeError, ValueError):
                result = _dispatch.dispatch(ignite_dataset, cache_name=cache_name, host=host, port=port, local=local, part=part, page_size=page_size, schema=schema, permutation=permutation, name=name)
                if (result is not _dispatch.OpDispatcher.NOT_SUPPORTED):
                    return result
                raise
        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)
    try:
        (_, _, _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)
    except (TypeError, ValueError):
        result = _dispatch.dispatch(ignite_dataset, cache_name=cache_name, host=host, port=port, local=local, part=part, page_size=page_size, schema=schema, permutation=permutation, name=name)
        if (result is not _dispatch.OpDispatcher.NOT_SUPPORTED):
            return result
        raise
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = None
    _execute.record_gradient('IgniteDataset', _inputs_flat, _attrs, _result, name)
    (_result,) = _result
    return _result
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`.
@_dispatch.add_dispatch_list
@tf_export('ignite_dataset')
def ignite_dataset(cache_name, host, port, local, part, page_size, schema, permutation, name=None):
    'IgniteDataset that allows to get data from Apache Ignite.\n\n  Apache Ignite is a memory-centric distributed database, caching, and processing\n  platform for transactional, analytical, and streaming workloads, delivering\n  in-memory speeds at petabyte scale. This contrib package contains an\n  integration between Apache Ignite and TensorFlow. The integration is based on\n  tf.data from TensorFlow side and Binary Client Protocol from Apache Ignite side.\n  It allows to use Apache Ignite as a datasource for neural network training,\n  inference and all other computations supported by TensorFlow. Ignite Dataset\n  is based on Apache Ignite Binary Client Protocol.\n\n  Args:\n    cache_name: A `Tensor` of type `string`. Ignite Cache Name.\n    host: A `Tensor` of type `string`. Ignite Thin Client Host.\n    port: A `Tensor` of type `int32`. Ignite Thin Client Port.\n    local: A `Tensor` of type `bool`.\n      Local flag that defines that data should be fetched from local host only.\n    part: A `Tensor` of type `int32`. Partition data should be fetched from.\n    page_size: A `Tensor` of type `int32`. Page size for Ignite Thin Client.\n    schema: A `Tensor` of type `int32`.\n      Internal structure that defines schema of cache objects.\n    permutation: A `Tensor` of type `int32`.\n      Internal structure that defines permutation of cache objects.\n    name: A name for the operation (optional).\n\n  Returns:\n    A `Tensor` of type `variant`.\n  '
    _ctx = (_context._context or _context.context())
    if ((_ctx is not None) and _ctx._thread_local_data.is_eager):
        try:
            _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(_ctx._context_handle, _ctx._thread_local_data.device_name, 'IgniteDataset', name, _ctx.post_execution_callbacks, cache_name, host, port, local, part, page_size, schema, permutation)
            return _result
        except _core._FallbackException:
            try:
                return ignite_dataset_eager_fallback(cache_name, host, port, local, part, page_size, schema, permutation, name=name, ctx=_ctx)
            except _core._SymbolicException:
                pass
            except (TypeError, ValueError):
                result = _dispatch.dispatch(ignite_dataset, cache_name=cache_name, host=host, port=port, local=local, part=part, page_size=page_size, schema=schema, permutation=permutation, name=name)
                if (result is not _dispatch.OpDispatcher.NOT_SUPPORTED):
                    return result
                raise
        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)
    try:
        (_, _, _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)
    except (TypeError, ValueError):
        result = _dispatch.dispatch(ignite_dataset, cache_name=cache_name, host=host, port=port, local=local, part=part, page_size=page_size, schema=schema, permutation=permutation, name=name)
        if (result is not _dispatch.OpDispatcher.NOT_SUPPORTED):
            return result
        raise
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = None
    _execute.record_gradient('IgniteDataset', _inputs_flat, _attrs, _result, name)
    (_result,) = _result
    return _result