Support aten::bucketize for pytorch models #23328 (#23527)

### Closes [#23328
](#23328)
### Details:
 - Support aten::bucketize for pytorch models

src/frontends/pytorch/src/op/bucketize.cpp

@@ -0,0 +1,50 @@
+// Copyright (C) 2018-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "openvino/op/bucketize.hpp"
+
+#include "openvino/frontend/pytorch/node_context.hpp"
+#include "openvino/op/add.hpp"
+#include "openvino/op/concat.hpp"
+#include "openvino/op/convert_like.hpp"
+#include "openvino/op/logical_or.hpp"
+#include "openvino/op/multiply.hpp"
+#include "utils.hpp"
+
+namespace ov {
+namespace frontend {
+namespace pytorch {
+namespace op {
+
+using namespace ov::op;
+
+OutputVector translate_bucketize(const NodeContext& context) {
+    num_inputs_check(context, 2, 5);
+    auto input = context.get_input(0);
+    auto boundaries = context.get_input(1);
+
+    element::Type output_type = ov::element::i64;
+    if (!context.input_is_none(2) && context.const_input<bool>(2)) {
+        output_type = ov::element::i32;
+    }
+
+    bool with_right_bound = true;
+    if (!context.input_is_none(3)) {
+        with_right_bound = !context.const_input<bool>(3);
+    }
+
+    auto bucketize =
+        context.mark_node(std::make_shared<v3::Bucketize>(input, boundaries, output_type, with_right_bound));
+
+    if (!context.input_is_none(4)) {
+        context.mutate_input(4, bucketize);
+    }
+
+    return {bucketize};
+};
+
+}  // namespace op
+}  // namespace pytorch
+}  // namespace frontend
+}  // namespace ov

src/frontends/pytorch/src/op_table.cpp

@@ -47,6 +47,7 @@ OP_CONVERTER(translate_bitwise_and);
 OP_CONVERTER(translate_bitwise_not);
 OP_CONVERTER(translate_bitwise_or);
 OP_CONVERTER(translate_bitwise_xor);
+OP_CONVERTER(translate_bucketize);
 OP_CONVERTER(translate_cat);
 OP_CONVERTER(translate_cdist);
 OP_CONVERTER(translate_celu);
@@ -374,6 +375,7 @@ const std::map<std::string, CreatorFunction> get_supported_ops_ts() {
         {"aten::Bool", op::translate_bool},
         // aten::broadcast_tensors - Supported in limited set of patterns
         {"aten::broadcast_to", op::translate_expand},
+        {"aten::bucketize", op::translate_bucketize},
         {"aten::cat", op::translate_cat},
         {"aten::cdist", op::translate_cdist},
         {"aten::ceil", op::optional_out<op::translate_1to1_match_1_inputs<opset10::Ceiling>, 1>},

tests/layer_tests/pytorch_tests/test_bucketize.py

@@ -0,0 +1,53 @@
+# Copyright (C) 2018-2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np
+import pytest
+import torch
+
+from pytorch_layer_test_class import PytorchLayerTest
+
+
+class TestBucketize(PytorchLayerTest):
+
+    def _prepare_input(self, input_shape, boundaries_range, input_dtype, boundaries_dtype):
+        return (
+            np.random.randn(*input_shape).astype(input_dtype), 
+            np.arange(*boundaries_range).astype(boundaries_dtype))
+
+    def create_model(self, out_int32, right, is_out):
+        class aten_bucketize(torch.nn.Module):
+
+            def __init__(self, out_int32, right, is_out) -> None:
+                super().__init__()
+                self.out_int32 = out_int32
+                self.right = right 
+                self.is_out = is_out
+
+            def forward(self, input, boundaries):
+                if self.is_out:
+                    output_dtype = torch.int32 if self.out_int32 else torch.int64
+                    output = torch.zeros_like(input, dtype=output_dtype)
+                    torch.bucketize(input, boundaries, out_int32=self.out_int32, right=self.right, out=output)
+                    return output
+                else:
+                    return torch.bucketize(input, boundaries, out_int32=self.out_int32, right=self.right)
+
+        ref_net = None
+
+        return aten_bucketize(out_int32, right, is_out), ref_net, "aten::bucketize"
+
+    @pytest.mark.nightly
+    @pytest.mark.precommit
+    @pytest.mark.parametrize("out_int32", [True, False])
+    @pytest.mark.parametrize("right", [True, False])
+    @pytest.mark.parametrize("is_out", [True, False])
+    @pytest.mark.parametrize("input_shape", [[1, ], [2, 1], [2, 2, 1]])
+    @pytest.mark.parametrize("input_dtype", ["float32", "int32"])
+    @pytest.mark.parametrize("boundaries_range", [[1, 10], (100, 200)])
+    @pytest.mark.parametrize("boundaries_dtype", ["float32", "int32"])
+    def test_bucketize(self, input_shape, boundaries_range, input_dtype, boundaries_dtype, out_int32, right, is_out, ie_device, precision, ir_version):
+        self._test(*self.create_model(out_int32, right, is_out), ie_device, precision, ir_version, kwargs_to_prepare_input={
+            "input_shape": input_shape, "input_dtype": input_dtype,
+            "boundaries_range": boundaries_range, "boundaries_dtype": boundaries_dtype,
+        })