[TF FE] Support ApproximateEqual operation for TensorFlow (openvinoto…

…olkit#23351)

### Details:
 - *Adding operation support for ApproximateEqual operation*
 - *Addresses issue openvinotoolkit#22082 *

---------

Co-authored-by: Roman Kazantsev <[email protected]>

src/frontends/tensorflow/docs/supported_ops.md

@@ -57,7 +57,7 @@ A "supported operation" is one that TensorFlow Frontend can convert to the OpenV
 | ApplyProximalGradientDescent                            | NO                            |                               |
 | ApplyRMSProp                                            | NO                            |                               |
 | ApproxTopK                                              | NO                            |                               |
-| ApproximateEqual                                        | NO                            |                               |
+| ApproximateEqual                                        | YES                           |                               |
 | ArgMax                                                  | YES                           |                               |
 | ArgMin                                                  | YES                           |                               |
 | AsString                                                | NO                            |                               |

src/frontends/tensorflow/src/op_table.cpp

@@ -422,6 +422,7 @@ const std::map<std::string, CreatorFunction> get_supported_ops() {
         {"AssignVariableOp", CreatorFunction(translate_assignvariable_op)},
         {"AssignAddVariableOp", CreatorFunction(translate_add_variable_op)},
         {"AssignSubVariableOp", CreatorFunction(translate_sub_variable_op)},
+        {"ApproximateEqual", CreatorFunction(translate_approximate_equal_op)},
         {"IsVariableInitialized", CreatorFunction(translate_varisinitialized_op)},
         {"MergeV2Checkpoints", CreatorFunction(translate_identity_op)},
         {"ReadVariableOp", CreatorFunction(translate_readvariable_op)},

src/frontends/tensorflow_common/include/common_op_table.hpp

@@ -33,6 +33,7 @@ OP_T_CONVERTER(translate_binary_op);
 OP_T_CONVERTER(translate_direct_reduce_op);
 OP_CONVERTER(translate_addv2_op);
 OP_CONVERTER(translate_add_n_op);
+OP_CONVERTER(translate_approximate_equal_op);
 OP_CONVERTER(translate_adjust_contrast_op);
 OP_CONVERTER(translate_arg_max_op);
 OP_CONVERTER(translate_arg_min_op);

src/frontends/tensorflow_common/src/op/approximate_equal_op.cpp

@@ -0,0 +1,37 @@
+// Copyright (C) 2018-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "common_op_table.hpp"
+#include "openvino/op/abs.hpp"
+#include "openvino/op/constant.hpp"
+#include "openvino/op/less.hpp"
+#include "openvino/op/subtract.hpp"
+
+using namespace std;
+using namespace ov::op;
+
+namespace ov {
+namespace frontend {
+namespace tensorflow {
+namespace op {
+
+OutputVector translate_approximate_equal_op(const NodeContext& node) {
+    default_op_checks(node, 2, {"ApproximateEqual"});
+    auto x = node.get_input(0);
+    auto y = node.get_input(1);
+    auto tolerance_value = node.get_attribute<float>("tolerance", 1e-5f);
+    auto tolerance = create_same_type_const_scalar<float>(x, tolerance_value);
+    // Implement the logic for ApproximateEqual
+    auto difference = make_shared<v1::Subtract>(x, y);
+    auto absolute = make_shared<v0::Abs>(difference);
+    auto is_less = make_shared<v1::Less>(absolute, tolerance);
+
+    // Create and return the corresponding OpenVINO operation
+    set_node_name(node.get_name(), is_less);
+    return {is_less};
+}
+}  // namespace op
+}  // namespace tensorflow
+}  // namespace frontend
+}  // namespace ov

tests/layer_tests/tensorflow_tests/test_tf_ApproximateEqual.py

@@ -0,0 +1,45 @@
+# Copyright (C) 2018-2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+import numpy as np
+import tensorflow as tf
+import pytest
+from common.tf_layer_test_class import CommonTFLayerTest
+
+class TestApproximateEqual(CommonTFLayerTest):
+    def _prepare_input(self, inputs_info):
+        rng = np.random.default_rng()
+        assert 'tensor1:0' in inputs_info
+        assert 'tensor2:0' in inputs_info
+        tensor1_shape = inputs_info['tensor1:0']
+        tensor2_shape = inputs_info['tensor2:0']
+        inputs_data = {}
+        inputs_data['tensor1:0'] = 4 * rng.random(tensor1_shape).astype(np.float32) - 2
+        inputs_data['tensor2:0'] = 4 * rng.random(tensor2_shape).astype(np.float32) - 2
+        return inputs_data
+
+    def create_approximate_equal_net(self, input1_shape, input2_shape):
+        tf.compat.v1.reset_default_graph()
+        # Create the graph and model
+        with tf.compat.v1.Session() as sess:
+            tensor1 = tf.compat.v1.placeholder(tf.float32, input1_shape, 'tensor1')
+            tensor2 = tf.compat.v1.placeholder(tf.float32, input2_shape, 'tensor2')
+            approx_equal_op = tf.raw_ops.ApproximateEqual(x=tensor1, y=tensor2, tolerance=0.01)
+            tf.compat.v1.global_variables_initializer()
+            tf_net = sess.graph_def
+
+        return tf_net, None
+
+    test_data_basic = [
+        dict(input1_shape=[2, 3], input2_shape=[2, 3]),
+        dict(input1_shape=[3, 4, 5], input2_shape=[3, 4, 5]),
+        dict(input1_shape=[1, 2, 3, 4], input2_shape=[1, 2, 3, 4]),
+    ]
+
+    @pytest.mark.parametrize("params", test_data_basic)
+    @pytest.mark.precommit_tf_fe
+    @pytest.mark.nightly
+    def test_approximate_equal_basic(self, params, ie_device, precision, ir_version, temp_dir,
+                                     use_legacy_frontend):
+        self._test(*self.create_approximate_equal_net(**params),
+                   ie_device, precision, ir_version, temp_dir=temp_dir,
+                   use_legacy_frontend=use_legacy_frontend)