diff --git a/TrainingExtensions/torch/src/python/aimet_torch/v2/quantization/tensor.py b/TrainingExtensions/torch/src/python/aimet_torch/v2/quantization/tensor.py
index 7fb389219a8..ed6643b38b0 100644
--- a/TrainingExtensions/torch/src/python/aimet_torch/v2/quantization/tensor.py
+++ b/TrainingExtensions/torch/src/python/aimet_torch/v2/quantization/tensor.py
@@ -90,6 +90,14 @@ class QuantizedTensorBase(torch.Tensor):
 
     encoding: EncodingBase
 
+    _attr_descriptors = {
+        torch.Tensor.dtype.__get__,
+        torch.Tensor.device.__get__,
+        torch.Tensor.layout.__get__,
+        torch.Tensor.shape.__get__,
+        torch.Tensor.size,
+    }
+
     _cast_ops = {
         torch.Tensor.half,
         torch.Tensor.float,
@@ -298,6 +306,9 @@ def __torch_function__(cls, func, types, args=(), kwargs=None): # pylint: disabl
             return HANDLED_FUNCTIONS[func](*args, **kwargs)
         ret = super().__torch_function__(func, types, args, kwargs)
 
+        if func in cls._attr_descriptors:
+            return ret
+
         self, *_ = args
 
         if not isinstance(self, QuantizedTensorBase):
diff --git a/TrainingExtensions/torch/test/python/v2/quantization/test_tensor.py b/TrainingExtensions/torch/test/python/v2/quantization/test_tensor.py
index 704d04107c0..beffdc8291d 100644
--- a/TrainingExtensions/torch/test/python/v2/quantization/test_tensor.py
+++ b/TrainingExtensions/torch/test/python/v2/quantization/test_tensor.py
@@ -687,3 +687,33 @@ def test_use_qtensor_arg_for_passthrough_op(self, qtensor_cls, callback, scale,
         for output in outputs:
             assert not isinstance(output, QuantizedTensorBase)
             assert not hasattr(output, 'encoding')
+
+    @pytest.mark.parametrize('qtensor_cls', [QuantizedTensor, DequantizedTensor])
+    def test_attribute_descriptor(self, qtensor_cls):
+        """
+        Given: torch.Tensor and a quantized/dequantized tensor with same dtype, device, shape, ...
+        When: Access the following attributes
+            * dtype
+            * device
+            * layout
+            * shape
+            * size()
+        Then: The attributes from both tensors should be value-equal and type-equal
+        """
+        tensor = torch.empty(10, 10)
+        qtensor = torch.empty(10, 10).as_subclass(qtensor_cls)
+
+        assert tensor.dtype == qtensor.dtype
+        assert type(tensor.dtype) == type(qtensor.dtype)
+
+        assert tensor.device == qtensor.device
+        assert type(tensor.device) == type(qtensor.device)
+
+        assert tensor.layout == qtensor.layout
+        assert type(tensor.layout) == type(qtensor.layout)
+
+        assert tensor.shape == qtensor.shape
+        assert type(tensor.shape) == type(qtensor.shape)
+
+        assert tensor.size() == qtensor.size()
+        assert type(tensor.size()) == type(qtensor.size())