[PT FE] Support aten::lerp and aten::lerp_ (#27272)

### Details:
 - *Support `aten::lerp` and `aten::lerp_`*

### Tickets:
 - *CVS-156191*

src/frontends/pytorch/src/op/lerp.cpp

@@ -0,0 +1,36 @@
+// Copyright (C) 2018-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "openvino/frontend/pytorch/node_context.hpp"
+#include "openvino/op/add.hpp"
+#include "openvino/op/convert_like.hpp"
+#include "openvino/op/multiply.hpp"
+#include "openvino/op/subtract.hpp"
+#include "utils.hpp"
+
+namespace ov {
+namespace frontend {
+namespace pytorch {
+namespace op {
+
+using namespace ov::op;
+
+OutputVector translate_lerp(const NodeContext& context) {
+    // Tensor = aten::lerp(%lhs.1, %rhs.1, %self.weight)
+    num_inputs_check(context, 3, 3);
+    Output<Node> start;
+    Output<Node> end;
+    std::tie(start, end) = get_inputs_with_promoted_types(context, 0, 1);
+
+    Output<Node> weight = context.get_input(2);
+    auto scale = context.mark_node(std::make_shared<v1::Subtract>(end, start));
+    weight = context.mark_node(std::make_shared<v1::ConvertLike>(weight, scale));
+    auto delta = context.mark_node(std::make_shared<v1::Multiply>(scale, weight));
+    return {context.mark_node(std::make_shared<v1::Add>(start, delta))};
+};
+
+}  // namespace op
+}  // namespace pytorch
+}  // namespace frontend
+}  // namespace ov

src/frontends/pytorch/src/op_table.cpp

@@ -121,6 +121,7 @@ OP_CONVERTER(translate_inverse);
 OP_CONVERTER(translate_is_nonzero);
 OP_CONVERTER(translate_layer_norm);
 OP_CONVERTER(translate_len);
+OP_CONVERTER(translate_lerp);
 OP_CONVERTER(translate_linalg_cross);
 OP_CONVERTER(translate_linalg_norm);
 OP_CONVERTER(translate_linalg_matrix_norm);
@@ -509,6 +510,7 @@ const std::unordered_map<std::string, CreatorFunction> get_supported_ops_ts() {
         {"aten::le", op::translate_1to1_match_2_inputs_align_types<opset10::LessEqual>},
         {"aten::leaky_relu", op::translate_1to1_match_2_inputs<opset10::PRelu>},
         {"aten::len", op::translate_len},
+        {"aten::lerp", op::translate_lerp},
         // lift op is torchscript specific op responsible for tensors coping with guarantee of new memory allocation
         {"aten::lift", op::skip_node},
         {"aten::lift_fresh", op::skip_node},

tests/layer_tests/pytorch_tests/test_lerp.py

@@ -0,0 +1,56 @@
+# 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, skip_if_export
+
+
+class TestLerp(PytorchLayerTest):
+    def _prepare_input(self):
+        return (np.random.randn(2, 5, 3, 4).astype(np.float32), self.input_rhs)
+
+    def create_model(self, weight, op_type):
+        class aten_lerp(torch.nn.Module):
+            def __init__(self, weight, op) -> None:
+                super().__init__()
+                self.weight = weight
+                self.forward = self.forward1 if op == "lerp" else self.forward2
+
+            def forward1(self, lhs, rhs):
+                return torch.lerp(lhs, rhs, weight=self.weight)
+
+            def forward2(self, lhs, rhs):
+                return lhs.lerp_(rhs, weight=self.weight)
+
+        return aten_lerp(weight, op_type), None, f"aten::{op_type}"
+
+    @pytest.mark.parametrize("weight", (-0.5,
+                                        0,
+                                        0.5,
+                                        1,
+                                        2,
+                                        skip_if_export([1, 5, 3, 4]))
+                             )
+    @pytest.mark.parametrize("input_shape_rhs", [[2, 5, 3, 4],
+                                                 [1, 5, 3, 4],
+                                                 [1]])
+    @pytest.mark.parametrize("op_type", ["lerp", "lerp_"])
+    @pytest.mark.nightly
+    @pytest.mark.precommit
+    @pytest.mark.precommit_torch_export
+    @pytest.mark.precommit_fx_backend
+    def test_lerp(self, ie_device, precision, ir_version,
+                  weight, input_shape_rhs, op_type):
+        self.input_rhs = np.random.randn(*input_shape_rhs).astype(np.float32)
+        if isinstance(weight, list):
+            weight = torch.rand(weight)
+        self._test(
+            *self.create_model(weight, op_type),
+            ie_device,
+            precision,
+            ir_version,
+            use_convert_model=True,
+        )