chore: import upstream snapshot with attribution

libnd4j/include/ops/declarable/helpers/cpu/gradient.cpp

@@ -0,0 +1,42 @@
+/* ******************************************************************************
+ *
+ *
+ * This program and the accompanying materials are made available under the
+ * terms of the Apache License, Version 2.0 which is available at
+ * https://www.apache.org/licenses/LICENSE-2.0.
+ *
+ *  See the NOTICE file distributed with this work for additional
+ *  information regarding copyright ownership.
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations
+ * under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ ******************************************************************************/
+
+//
+//  @author sgazeos@gmail.com
+//
+#include <ops/declarable/helpers/axis.h>
+#include <system/op_boilerplate.h>
+
+namespace sd {
+namespace ops {
+namespace helpers {
+template <typename T>
+static void applyGradientDescent_(NDArray* input, NDArray* step, double weight, NDArray* output) {
+  auto lambda = LAMBDA_TT(_x, _y, weight) { return _x - (_y * weight); });
+
+  input->applyPairwiseLambda<T>(step, lambda, output);
+}
+
+void applyGradientDescent(sd::LaunchContext* context, NDArray* input, NDArray* step, double weight, NDArray* output) {
+  BUILD_SINGLE_SELECTOR(input->dataType(), applyGradientDescent_, (input, step, weight, output), SD_FLOAT_TYPES);
+}
+BUILD_SINGLE_TEMPLATE( void applyGradientDescent_,
+                      (NDArray * input, NDArray* step, double weight, NDArray* output), SD_FLOAT_TYPES);
+}  // namespace helpers
+}  // namespace ops
+}  // namespace sd