[PT FE]: Add support for prim::data operation

src/frontends/pytorch/src/op_table.cpp

@@ -360,7 +360,6 @@ OP_CONVERTER(translate_embedding_ext);
 OP_CONVERTER(translate_linear_awq);
 OP_CONVERTER(translate_linear_bitnet);
 OP_CONVERTER(translate_linear_ext);
-
 }  // namespace op
 
 // Supported ops for TorchScript
@@ -795,6 +794,7 @@ const std::unordered_map<std::string, CreatorFunction> get_supported_ops_ts() {
         {"prim::TupleIndex", op::translate_tuple_index},
         // prim::TupleUnpack - Supported in limited set of patterns
         {"prim::type", op::skip_node},  // Used with prim::device, pass PtFrameworkNode.
+        {"prim::data", op::skip_node},
         {"quantized::add", op::translate_quantized_add},
         {"quantized::add_relu", op::translate_quantized_add_relu},
         {"quantized::cat", op::translate_quantized_cat},

tests/layer_tests/pytorch_tests/test_data.py

@@ -0,0 +1,71 @@
+# Copyright (C) 2018-2025 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np
+import pytest
+import torch
+
+from pytorch_layer_test_class import PytorchLayerTest
+
+
+class Model(torch.nn.Module):
+    def forward(self, x):
+        return x.data
+
+
+class ModelGrad(torch.nn.Module):
+    def forward(self, x):
+        y = x * 2.5
+        return y.data
+
+
+class TestPrimData(PytorchLayerTest):
+    def _prepare_input(self):
+        np.random.seed(self.seed)
+        if self.dtype in (torch.complex64, torch.complex128):
+            real = (np.random.randn(*self.shape) * 10).astype(np.float32)
+            imag = (np.random.randn(*self.shape) * 10).astype(np.float32)
+            data = real + 1j * imag
+            data = data.astype(np.complex128 if self.dtype == torch.complex128 else np.complex64)
+        else:
+            data = (np.random.randn(*self.shape) * 10).astype(np.float32)
+        tensor = torch.from_numpy(data).to(self.dtype)
+        return (tensor.numpy(),)
+
+    @pytest.mark.parametrize("dtype", [torch.float32, torch.float64, torch.int32, torch.int64])
+    @pytest.mark.parametrize("shape", [[2, 3, 4], [1, 5], [10]])
+    def test_data_basic(self, shape, dtype, ie_device, precision, ir_version):
+        self.shape = shape
+        self.dtype = dtype
+        self.seed = 0
+        self._test(Model(), None, "prim::data", ie_device, precision, ir_version)
+
+    @pytest.mark.parametrize("dtype", [torch.float32, torch.int32])
+    def test_data_requires_grad(self, dtype, ie_device, precision, ir_version):
+        self.shape = (3, 2)
+        self.dtype = dtype
+        self.seed = 1
+        self._test(ModelGrad(), None, "prim::data", ie_device, precision, ir_version)
+
+    @pytest.mark.parametrize("dtype", [torch.complex64, torch.complex128])
+    @pytest.mark.parametrize("shape", [[2, 3], [1, 5], [4]])
+    @pytest.mark.xfail(
+        reason="OpenVINO frontend does not yet support complex tensor inputs",
+        raises=AssertionError,
+    )
+    def test_data_complex(self, shape, dtype, ie_device, precision, ir_version):
+        self.shape = shape
+        self.dtype = dtype
+        self.seed = 2
+        self._test(Model(), None, "prim::data", ie_device, precision, ir_version)
+
+    @pytest.mark.parametrize("dtype", [torch.complex64, torch.complex128])
+    @pytest.mark.xfail(
+        reason="OpenVINO frontend does not yet support complex tensor inputs",
+        raises=AssertionError,
+    )
+    def test_data_complex_requires_grad(self, dtype, ie_device, precision, ir_version):
+        self.shape = (2, 3)
+        self.dtype = dtype
+        self.seed = 3
+        self._test(ModelGrad(), None, "prim::data", ie_device, precision, ir_version)