return queryRescorerList.stream().map(r -> r.evaluate(params)).filter(b -> b != null).toArray(n -> new RescorerBuilder<?>[n]);
    }

    public void addQueryRescorer(final QueryRescorer rescorer) {
        queryRescorerList.add(rescorer);
    }

            params.put("query", query);
            params.put("responseFields", responseFields);
            params.put("offset", offset);
            params.put("size", size);
            // TODO support rescorer(convert to map)
            // params.put("geoInfo", geoInfo);
            // params.put("facetInfo", facetInfo);
            params.put("similarDocHash", similarDocHash);
            return params;
        }

// which contain the "real" checkpoint keys into the TensorBundle SSTable.
// They also contain the logic needed to take the restored tensors from
// RestoreV2 and load them back into the "object" they came from via their
// overridden "restore" method:
// https://github.com/tensorflow/tensorflow/blob/ddc1bbad3dfd4a089eb96014f26cc16664b1b2f8/tensorflow/python/training/saving/saveable_object.py#L85

	}
	rangeString, data := snapshottable.Snapshot()
	dst.Range = rangeString
	dst.Data = data
	return nil
}

// Restore restores the pool to the previously captured state. ErrMismatchedNetwork
// is returned if the provided port range doesn't exactly match the previous range.
func (r *PortAllocator) Restore(pr net.PortRange, data []byte) error {
	if pr.String() != r.portRange.String() {
		return ErrMismatchedNetwork
	}

        override fun toTracking(): Tracking {
            throw IllegalStateException("Cannot track state because it was restored")
        }

        override fun toRestoring(): Restoring {
            throw IllegalStateException("Cannot restore state because it was already restored")
        }

        fun loadFrom(storedState: CachedEnvironmentState) {
            if (storedState.cleared) {

    Arg<TF_StrTensor, [{shape {N}.  The names of the tensors to be restored. This is tensor name in the model.}]>:$tensor_names,
    Arg<TF_StrTensor, [{shape {N}.  The slice specs of the tensors to be restored.
Empty strings indicate that they are non-partitioned tensors.}]>:$shape_and_slices,
    // VarHandles for restored tensors.
    Variadic<TF_Tensor>:$var_handles,
    // Data types for restored tensor.
    TypeArrayAttr: $restored_dtypes,

  are multiple output tensors from one node). Since Retval nodes are not used in
  SavedModel, this function removes them and restore the names to the actual
  output tensors.

  Args:
    graph_def: the converted GraphDef.

  Returns:
    The GraphDef with Retval nodes removed and output tensor names restored.
  """
  output_renaming_map = {}
  with session.Session(graph=ops.Graph()):
    importer.import_graph_def(graph_def, name='')

        assertTrue(enabledOnOtherThread)
    }

    @Test
    fun `input tracking is restored to the state when it was disabled`() {
        val state = InputTrackingState()
        state.disableForCurrentThread() // <1>
        state.disableForCurrentThread() // <2>

        state.restoreForCurrentThread() // Revert call at <2>, restore to "disabled" state set by call at <1>
        assertFalse(state.isEnabledForCurrentThread())

/**
 * [KaSymbol] is valid only during read action it was created in
 * To pass the symbol from one read action to another the KtSymbolPointer should be used
 *
 * We can restore the symbol
 *  * for symbol which came from Kotlin source it will be restored based on [com.intellij.psi.SmartPsiElementPointer]
 *  * restoring symbols which came from Java source is not supported yet
 *  * for library symbols:

}

// Expiry returns expiry of restored object and true if restore-object has completed.
// Otherwise returns zero value of time.Time and false.
func (r restoreObjStatus) Expiry() (time.Time, bool) {
	if r.Ongoing() {
		return time.Time{}, false
	}
	return r.expiry, true
}

// Ongoing returns true if restore-object is ongoing.
func (r restoreObjStatus) Ongoing() bool {