[TF FE] Deduce Switch-Merge predicate shape (openvinotoolkit#27277)

**Details:** It helps to convert some TF models out-of-the-box with
static rank tensors that are required by plugins for inference.

**Ticket:** 156204

---------

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

src/common/transformations/src/transformations/common_optimizations/reverse_shape_and_type_infer.cpp

@@ -282,6 +282,15 @@ bool ov::pass::ReverseShapeAndTypeInfer::run_on_model(const std::shared_ptr<ov::
                 if_op->get_input_tensor(0).m_element_type = element::boolean;
                 is_changed = true;
             }
+
+            // in case TensorFlow models, we can deduce predicate shape that must be a scalar
+            // If operations created by fusing Switch-Merge sub-graph contain tf_switch_merge_if rt-info
+            if (if_op->get_rt_info().count("tf_switch_merge_if") &&
+                if_op->get_rt_info()["tf_switch_merge_if"].as<bool>() &&
+                if_op->input_value(0).get_partial_shape().rank().is_dynamic()) {
+                if_op->get_input_tensor(0).m_partial_shape = ov::PartialShape({});
+                is_changed = true;
+            }
         } else if (ov::as_type_ptr<ov::op::v1::ConvertLike>(op)) {
             is_changed |= inherit_output_shape(op, {0});
             is_changed |= inherit_output_type(op, {1});

src/frontends/tensorflow/src/transformations/switch_merge_resolve.cpp

@@ -235,6 +235,9 @@ bool pass::SwitchMergeResolver::run_on_model(const shared_ptr<Model>& m) {
         auto else_body = make_shared<Model>(else_results, else_params);
 
         auto if_op = make_shared<v8::If>(cond);
+        // in case TensorFlow models, we can deduce predicate shape that must be a scalar
+        if_op->get_rt_info()["tf_switch_merge_if"] = true;
+
         set_cf_marker(if_cf_marker, if_op);
         if_op->set_then_body(then_body);
         if_op->set_else_body(else_body);

tests/layer_tests/tensorflow_tests/test_tf_SwitchMerge.py

@@ -63,3 +63,48 @@ def test_merge_eliminating_several_cond_flows(self, params, cond_value, x_type,
         self._test(*self.merge_eliminating_several_cond_flows_net(**params, cond_value=cond_value, x_type=x_type),
                    ie_device, precision, ir_version, temp_dir=temp_dir,
                    use_legacy_frontend=use_legacy_frontend)
+
+
+class TestSwitchMergeWithVariablePredicate(CommonTFLayerTest):
+    def _prepare_input(self, inputs_info):
+        assert 'x:0' in inputs_info
+        x_shape = inputs_info['x:0']
+        inputs_data = {}
+        rng = np.random.default_rng()
+        inputs_data['x:0'] = rng.integers(-10, 10, x_shape).astype(np.float32)
+        inputs_data['cond:0'] = np.array(self.cond_value, dtype=bool)
+        return inputs_data
+
+    def switch_merge_with_variable_predicate_net(self, x_shape, cond_shape, cond_value):
+        self.cond_value = cond_value
+        tf.compat.v1.reset_default_graph()
+        # Create the graph and model
+        with tf.compat.v1.Session() as sess:
+            x = tf.compat.v1.placeholder(tf.float32, x_shape, 'x')
+            cond = tf.compat.v1.placeholder(tf.bool, cond_shape, 'cond')
+            const_add = tf.constant(3, dtype=tf.float32)
+            const_sub = tf.constant(1, dtype=tf.float32)
+            switch_false, switch_true = tf.raw_ops.Switch(data=x, pred=cond)
+            add = tf.raw_ops.AddV2(x=switch_false, y=const_add)
+            sub = tf.raw_ops.Sub(x=switch_true, y=const_sub)
+            merge = tf.raw_ops.Merge(inputs=[add, sub])
+            const_main = tf.constant(1, dtype=tf.float32)
+            tf.raw_ops.AddV2(x=merge[0], y=const_main, name='add_res')
+            tf.compat.v1.global_variables_initializer()
+            tf_net = sess.graph_def
+
+        return tf_net, None
+
+    @pytest.mark.parametrize('x_shape', [[], [2], [3, 2]])
+    @pytest.mark.parametrize('cond_shape', [None, []])
+    @pytest.mark.parametrize('cond_value', [True, False])
+    @pytest.mark.precommit
+    @pytest.mark.nightly
+    def test_switch_merge_with_variable_predicate(self, x_shape, cond_shape, cond_value,
+                                                  ie_device, precision, ir_version, temp_dir,
+                                                  use_legacy_frontend):
+        if ie_device == 'GPU':
+            pytest.skip("156244: accuracy error on GPU")
+        self._test(*self.switch_merge_with_variable_predicate_net(x_shape, cond_shape, cond_value),
+                   ie_device, precision, ir_version, temp_dir=temp_dir,
+                   use_legacy_frontend=use_legacy_frontend)