[TF FE]Support Inv operation for TensorFlow models (#20720)

* [TF FE]Support Inv operation for TensorFlow models

* added test tests/layer_tests/tensorflow_tests/test_tf_Inv.py and src/frontends/tensorflow_common/src/op/inv.cpp

* Update tests/layer_tests/tensorflow_tests/test_tf_Inv.py

* Update tests/layer_tests/tensorflow_tests/test_tf_Inv.py

* Update tests/layer_tests/tensorflow_tests/test_tf_Inv.py

---------

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

src/frontends/tensorflow/src/op_table.cpp

@@ -194,6 +194,7 @@ const std::map<std::string, CreatorFunction> get_supported_ops() {
         {"HashTableV2", CreatorFunction(translate_hash_table_op)},
         {"Identity", CreatorFunction(translate_identity_op)},
         {"IdentityN", CreatorFunction(translate_identity_n_op)},
+        {"Inv", CreatorFunction(translate_inv_op)},
         {"If", CreatorFunction(translate_if_op)},
         {"input_arg", CreatorFunction(translate_input_arg_op)},
         {"Iterator", CreatorFunction(translate_iterator_op)},

src/frontends/tensorflow_common/include/common_op_table.hpp

@@ -76,6 +76,7 @@ OP_CONVERTER(translate_gather_tree_op);
 OP_CONVERTER(translate_identity_op);
 OP_CONVERTER(translate_identity_n_op);
 OP_CONVERTER(translate_input_arg_op);
+OP_CONVERTER(translate_inv_op);
 OP_CONVERTER(translate_invert_permutation_op);
 OP_CONVERTER(translate_output_arg_op);
 OP_CONVERTER(translate_interpolate_op);

src/frontends/tensorflow_common/src/op/inv.cpp

@@ -0,0 +1,30 @@
+// 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/divide.hpp"
+
+using namespace std;
+using namespace ov::op;
+
+namespace ov {
+namespace frontend {
+namespace tensorflow {
+namespace op {
+OutputVector translate_inv_op(const NodeContext& node) {
+    default_op_checks(node, 1, {"Inv"});
+    auto x = node.get_input(0);
+
+    // prepare auxiliary one constants of the same type as the inputs
+    auto one = create_same_type_const_scalar<int32_t>(x, 1);
+
+    auto inv = make_shared<v1::Divide>(one, x);
+    set_node_name(node.get_name(), inv);
+    return inv->outputs();
+}
+}  // namespace op
+}  // namespace tensorflow
+}  // namespace frontend
+}  // namespace ov

tests/layer_tests/tensorflow_tests/test_tf_Inv.py

@@ -0,0 +1,44 @@
+# 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 TestInv(CommonTFLayerTest):
+    def _prepare_input(self, inputs_info):
+        assert 'x' in inputs_info
+        x_shape = inputs_info['x']
+        inputs_data = {}
+        inputs_data['x'] = np.random.choice([-5, -4, -3, -2, -1, 1, 2, 3, 4, 5], x_shape).astype(np.float32)
+
+        return inputs_data
+
+    def create_inv_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')
+            tf.raw_ops.Inv(x=x)
+            tf.compat.v1.global_variables_initializer()
+            tf_net = sess.graph_def
+
+        return tf_net, None
+
+    test_data_basic = [
+        dict(input_shape=[], input_type=np.float32),
+        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_inv_basic(self, params, ie_device, precision, ir_version, temp_dir,
+                                         use_new_frontend, use_old_api):
+        self._test(*self.create_inv_net(**params),
+                   ie_device, precision, ir_version, temp_dir=temp_dir,
+                   use_new_frontend=use_new_frontend, use_old_api=use_old_api)