[TF FE] Refactor Range and add layer test (#15548)

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

src/frontends/tensorflow_common/src/op/range.cpp

@@ -14,14 +14,14 @@ namespace tensorflow {
 namespace op {
 
 OutputVector translate_range_op(const NodeContext& node) {
+    default_op_checks(node, 3, {"Range", "RANGE"});
     auto start = node.get_input(0);
-    auto stop = node.get_input(1);
-    auto step = node.get_input(2);
-    auto out_type = node.get_attribute<ov::element::Type>("Tidx");
+    auto limit = node.get_input(1);
+    auto delta = node.get_input(2);
 
-    auto res = make_shared<Range>(start, stop, step, out_type);
-    set_node_name(node.get_name(), res);
-    return res->outputs();
+    auto range = make_shared<Range>(start, limit, delta, start.get_element_type());
+    set_node_name(node.get_name(), range);
+    return {range};
 }
 
 }  // namespace op

tests/layer_tests/tensorflow_tests/test_tf_Range.py

@@ -0,0 +1,58 @@
+# 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 TestRange(CommonTFLayerTest):
+    def _prepare_input(self, inputs_info):
+        inputs_data = {}
+        if self.negative_delta:
+            inputs_data['start'] = np.random.randint(1, 10, []).astype(self.input_type)
+            inputs_data['limit'] = np.random.randint(-10, 0, []).astype(self.input_type)
+            inputs_data['delta'] = np.random.randint(-5, -1, []).astype(self.input_type)
+        else:
+            inputs_data['start'] = np.random.randint(1, 10, []).astype(self.input_type)
+            inputs_data['limit'] = np.random.randint(10, 30, []).astype(self.input_type)
+            inputs_data['delta'] = np.random.randint(1, 5, []).astype(self.input_type)
+
+        return inputs_data
+
+    def create_range_net(self, input_type, negative_delta):
+        self.input_type = input_type
+        self.negative_delta = negative_delta
+        types_map = {
+            np.float32: tf.float32,
+            np.int32: tf.int32
+        }
+        assert input_type in types_map, "Incorrect test case"
+        tf_type = types_map[input_type]
+        tf.compat.v1.reset_default_graph()
+        # Create the graph and model
+        with tf.compat.v1.Session() as sess:
+            start = tf.compat.v1.placeholder(tf_type, [], 'start')
+            limit = tf.compat.v1.placeholder(tf_type, [], 'limit')
+            delta = tf.compat.v1.placeholder(tf_type, [], 'delta')
+            tf.raw_ops.Range(start=start, limit=limit, delta=delta)
+            tf.compat.v1.global_variables_initializer()
+
+            tf_net = sess.graph_def
+
+        return tf_net, None
+
+    test_data_basic = [
+        dict(input_type=np.float32, negative_delta=False),
+        dict(input_type=np.int32, negative_delta=True),
+    ]
+
+    @pytest.mark.parametrize("params", test_data_basic)
+    @pytest.mark.precommit_tf_fe
+    @pytest.mark.nightly
+    def test_range_basic(self, params, ie_device, precision, ir_version, temp_dir,
+                         use_new_frontend, use_old_api):
+        self._test(*self.create_range_net(**params),
+                   ie_device, precision, ir_version, temp_dir=temp_dir,
+                   use_new_frontend=use_new_frontend, use_old_api=use_old_api)