[TF FE][Opset13] Enable Multinomial operator in TF frontend (#20646)

* Enable Multinomial operator in TF frontend

* Implement requested changes

* Update tests/layer_tests/tensorflow_tests/test_tf_Multinomial.py

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

* Align with CPU implementation

---------

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

tests/layer_tests/pytest.ini

@@ -2,6 +2,7 @@
 markers =
     nightly
     precommit
+    precommit_tf_fe
     precommit_ts_backend
     precommit_fx_backend
     timeout

tests/layer_tests/tensorflow_tests/test_tf_Multinomial.py

@@ -0,0 +1,144 @@
+# Copyright (C) 2018-2023 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import pytest
+import tensorflow as tf
+from common.tf_layer_test_class import CommonTFLayerTest
+import numpy as np
+
+
+class TestMultinomial(CommonTFLayerTest):
+    def _prepare_input(self, inputs_dict, kwargs):
+        inputs_dict["num_samples"] = np.array(kwargs["num_samples"], dtype=np.int32)
+        inputs_dict["probs"] = kwargs["input"]
+        return inputs_dict
+
+    def create_tf_multinomial_net_shape(
+        self, global_seed, op_seed, logits_shape, input_type, out_type
+    ):
+        tf.compat.v1.reset_default_graph()
+        # Configuration required to make multinomial randomness predictable across devices, results depends on TF parallel execution.
+        session_conf = tf.compat.v1.ConfigProto(
+            intra_op_parallelism_threads=1, inter_op_parallelism_threads=1
+        )
+        # Create the graph and model
+        with tf.compat.v1.Session(config=session_conf) as sess:
+            probs = tf.compat.v1.placeholder(input_type, logits_shape, "probs")
+            num_samples = tf.compat.v1.placeholder(tf.int32, [], "num_samples")
+            if global_seed is not None:
+                tf.random.set_seed(global_seed)
+            tf.raw_ops.ZerosLike(
+                x=tf.raw_ops.Multinomial(
+                    logits=tf.math.log(probs),
+                    num_samples=num_samples,
+                    seed=global_seed,
+                    seed2=op_seed,
+                    output_dtype=out_type,
+                )
+            )
+
+            tf.compat.v1.global_variables_initializer()
+            tf_net = sess.graph_def
+
+        return tf_net, None
+
+    def create_tf_multinomial_net_exact(
+        self, global_seed, op_seed, logits_shape, input_type, out_type
+    ):
+        tf.compat.v1.reset_default_graph()
+        # Configuration required to make multinomial randomness predictable across devices, results depends on TF parallel execution.
+        session_conf = tf.compat.v1.ConfigProto(
+            intra_op_parallelism_threads=1, inter_op_parallelism_threads=1
+        )
+        # Create the graph and model
+        with tf.compat.v1.Session(config=session_conf) as sess:
+            probs = tf.compat.v1.placeholder(input_type, logits_shape, "probs")
+            num_samples = tf.compat.v1.placeholder(tf.int32, [], "num_samples")
+            if global_seed is not None:
+                tf.random.set_seed(global_seed)
+                tf.raw_ops.Multinomial(
+                    logits=tf.math.log(probs),
+                    num_samples=num_samples,
+                    seed=global_seed,
+                    seed2=op_seed,
+                    output_dtype=out_type,
+                )
+
+            tf.compat.v1.global_variables_initializer()
+            tf_net = sess.graph_def
+
+        return tf_net, None
+
+    @pytest.mark.parametrize("out_type", [tf.int32, tf.int64])
+    @pytest.mark.parametrize(
+        ("input", "num_samples", "seed", "test_type"),
+        [
+            (
+                np.array([[0, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0]], dtype=np.float32),
+                1024,
+                [32465, 48971],
+                "exact",
+            ),
+            (
+                np.array(
+                    [
+                        [0.001, 0.001, 0.1, 0.9],
+                        [5, 10, 1e-5, 256],
+                        [1, 1e-5, 1e-5, 1e-5],
+                    ],
+                    dtype=np.float64,
+                ),
+                256,
+                [32465, 48971],
+                "shape",
+            ),
+            (np.array([[1, 1, 1, 1]], dtype=np.float16), 1024, [1, 1], "shape"),
+            (
+                np.array([[1, 2, 3, 4], [4, 3, 2, 1], [1, 0, 0, 0]], dtype=np.float32),
+                1,
+                [78132, None],
+                "shape",
+            ),
+            (
+                np.array([[7, 7, 7, 7], [7, 7, 7, 7], [7, 7, 7, 7]], dtype=np.float32),
+                1024,
+                [32465, None],
+                "shape",
+            ),
+        ],
+    )
+    @pytest.mark.nightly
+    @pytest.mark.precommit_tf_fe
+    def test_multinomial_basic(
+        self,
+        input,
+        num_samples,
+        seed,
+        out_type,
+        test_type,
+        ie_device,
+        precision,
+        ir_version,
+        temp_dir,
+        use_new_frontend,
+        use_old_api,
+    ):
+        if ie_device == "GPU":
+            pytest.skip("Multinomial is not supported on GPU")
+        net = getattr(self, f"create_tf_multinomial_net_{test_type}")
+        self._test(
+            *net(
+                global_seed=seed[0],
+                op_seed=seed[1],
+                logits_shape=input.shape,
+                input_type=input.dtype,
+                out_type=out_type,
+            ),
+            ie_device,
+            precision,
+            temp_dir=temp_dir,
+            ir_version=ir_version,
+            use_new_frontend=use_new_frontend,
+            use_old_api=use_old_api,
+            kwargs_to_prepare_input={"input": input, "num_samples": num_samples},
+        )