[TF FE][TF Hub] Support Xlogy operation (#20467)

* [TF FE][TF Hub] Support Xlogy operation

* fix

* fix

* fix

* fix

* Update tests/layer_tests/tensorflow_tests/test_tf_Xlogy.py

* Update tests/layer_tests/tensorflow_tests/test_tf_Xlogy.py

---------

Co-authored-by: Roman Kazantsev <roman.kazantsev@intel.com>

src/frontends/tensorflow/src/op_table.cpp

@@ -286,6 +286,7 @@ const std::map<std::string, CreatorFunction> get_supported_ops() {
         {"While", CreatorFunction(translate_while_op)},
         {"Where", CreatorFunction(translate_where_op)},
         {"Xdivy", CreatorFunction(translate_x_div_y_op)},
+        {"Xlogy", CreatorFunction(translate_xlogy_op)},
         {"ZerosLike", CreatorFunction(translate_zeros_like_op)},
 
         // Translators for SavedModel and MetaGraph

src/frontends/tensorflow_common/include/common_op_table.hpp

@@ -149,6 +149,7 @@ OP_CONVERTER(translate_unravel_index_op);
 OP_CONVERTER(translate_unsorted_segment_sum_op);
 OP_CONVERTER(translate_where_op);
 OP_CONVERTER(translate_x_div_y_op);
+OP_CONVERTER(translate_xlogy_op);
 OP_CONVERTER(translate_zeros_like_op);
 
 // Translators for internal operations

src/frontends/tensorflow_common/src/op/xlogy.cpp

@@ -0,0 +1,43 @@
+// Copyright (C) 2018-2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "common_op_table.hpp"
+#include "openvino/op/constant.hpp"
+#include "openvino/op/convert_like.hpp"
+#include "openvino/op/equal.hpp"
+#include "openvino/op/log.hpp"
+#include "openvino/op/multiply.hpp"
+#include "openvino/op/select.hpp"
+
+using namespace std;
+using namespace ov::opset10;
+
+namespace ov {
+namespace frontend {
+namespace tensorflow {
+namespace op {
+OutputVector translate_xlogy_op(const NodeContext& node) {
+    default_op_checks(node, 2, {"Xlogy"});
+    auto x = node.get_input(0);
+    auto y = node.get_input(1);
+
+    // prepare auxiliary zero constant of the same type as the input
+    auto zero = create_same_type_const_scalar<int32_t>(x, 0);
+
+    // compute a mask to identify where x is equal to 0
+    auto is_zero = make_shared<Equal>(x, zero);
+
+    // compute x * log(y) elementwise
+    auto xlog_y = make_shared<Multiply>(x, make_shared<Log>(y));
+
+    // create the output tensor using Select to handle the x == 0 condition
+    auto result = make_shared<Select>(is_zero, zero, xlog_y);
+
+    set_node_name(node.get_name(), result);
+    return result->outputs();
+}
+}  // namespace op
+}  // namespace tensorflow
+}  // namespace frontend
+}  // namespace ov

tests/layer_tests/tensorflow_tests/test_tf_Xlogy.py

@@ -0,0 +1,49 @@
+# Copyright (C) 2018-2023 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np
+import pytest
+import tensorflow as tf
+from common.tf_layer_test_class import CommonTFLayerTest
+
+
+class TestXlogy(CommonTFLayerTest):
+    def _prepare_input(self, inputs_info):
+        assert 'x' in inputs_info
+        assert 'y' in inputs_info
+        x_shape = inputs_info['x']
+        y_shape = inputs_info['y']
+        inputs_data = {}
+        # x = [-3 ,3] y = [1, 2]
+        # generate x in way to have zeros
+        inputs_data['x'] = (6 * np.random.random(size=x_shape).astype(np.float32) - 3) * \
+                            np.random.randint(2, size=x_shape).astype(np.float32)
+        inputs_data['y'] = np.random.random(size=y_shape).astype(np.float32) + 1
+        return inputs_data
+
+    def create_xlogy_net(self, input_shape, input_type):
+        self.input_type = input_type
+        tf.compat.v1.reset_default_graph()
+        # Create the graph and model
+        with tf.compat.v1.Session() as sess:
+            x = tf.compat.v1.placeholder(input_type, input_shape, 'x')
+            y = tf.compat.v1.placeholder(input_type, input_shape, 'y')
+            tf.raw_ops.Xlogy(x=x, y=y)
+            tf.compat.v1.global_variables_initializer()
+            tf_net = sess.graph_def
+
+        return tf_net, None
+
+    test_data_basic = [
+        dict(input_shape=[10, 20], input_type=np.float32),
+        dict(input_shape=[2, 3, 4], input_type=np.float32),
+    ]
+
+    @pytest.mark.parametrize("params", test_data_basic)
+    @pytest.mark.precommit_tf_fe
+    @pytest.mark.nightly
+    def test_xlogy_basic(self, params, ie_device, precision, ir_version, temp_dir,
+                         use_new_frontend, use_old_api):
+        self._test(*self.create_xlogy_net(**params),
+                   ie_device, precision, ir_version, temp_dir=temp_dir,
+                   use_new_frontend=use_new_frontend, use_old_api=use_old_api)