[TF FE] Simplify FakrQuantWithMinMaxVars translator and add layer test (#15927)

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

tests/layer_tests/tensorflow_tests/test_tf_FakeQuantWithMinMaxVars.py

@@ -0,0 +1,60 @@
+# 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 TestFakeQuantWithMinMaxVars(CommonTFLayerTest):
+    def _prepare_input(self, inputs_info):
+        # generate elements so that the input tensor may contain repeating elements
+        assert 'inputs' in inputs_info, "Test error: inputs_info must contain `input`"
+        inputs_shape = inputs_info['inputs']
+        inputs_data = {}
+        inputs_data['inputs'] = np.random.randint(-10, 10, inputs_shape).astype(np.float32)
+        return inputs_data
+
+    def create_fake_quant_with_min_max_vars_net(self, inputs_shape, min_value, max_value, num_bits, narrow_range,
+                                                fake_quant_op):
+        tf.compat.v1.reset_default_graph()
+        with tf.compat.v1.Session() as sess:
+            inputs = tf.compat.v1.placeholder(tf.float32, inputs_shape, 'inputs')
+            min = tf.constant(min_value, dtype=tf.float32)
+            max = tf.constant(max_value, dtype=tf.float32)
+            fake_quant_op(inputs=inputs, min=min, max=max, num_bits=num_bits,
+                          narrow_range=narrow_range)
+            tf.compat.v1.global_variables_initializer()
+            tf_net = sess.graph_def
+
+        return tf_net, None
+
+    test_basic = [
+        # test FakeQuantWithMinMaxVars
+        dict(inputs_shape=[2, 6, 4], min_value=-3, max_value=4, num_bits=None, narrow_range=None,
+             fake_quant_op=tf.raw_ops.FakeQuantWithMinMaxVars),
+        dict(inputs_shape=[3, 2, 1, 5], min_value=-4, max_value=5, num_bits=14, narrow_range=True,
+             fake_quant_op=tf.raw_ops.FakeQuantWithMinMaxVars),
+        dict(inputs_shape=[3, 2, 4], min_value=2, max_value=4, num_bits=10, narrow_range=False,
+             fake_quant_op=tf.raw_ops.FakeQuantWithMinMaxVars),
+        dict(inputs_shape=[1, 2, 3], min_value=-6, max_value=-3, num_bits=8, narrow_range=True,
+             fake_quant_op=tf.raw_ops.FakeQuantWithMinMaxVars),
+
+        # test FakeQuantWithMinMaxVarsPerChannel
+        pytest.param(dict(inputs_shape=[2, 6, 4], min_value=[-4, -3, -5, -8], max_value=[4, 7, 9, 5], num_bits=None,
+                          narrow_range=None,
+                          fake_quant_op=tf.raw_ops.FakeQuantWithMinMaxVarsPerChannel),
+                     marks=pytest.mark.xfail(reason="104822"))
+
+    ]
+
+    @pytest.mark.parametrize("params", test_basic)
+    @pytest.mark.precommit_tf_fe
+    @pytest.mark.nightly
+    def test_fake_quant_with_min_max_vars_basic(self, params, ie_device, precision, ir_version, temp_dir,
+                                                use_new_frontend,
+                                                use_old_api):
+        self._test(*self.create_fake_quant_with_min_max_vars_net(**params),
+                   ie_device, precision, ir_version, temp_dir=temp_dir,
+                   use_new_frontend=use_new_frontend, use_old_api=use_old_api)