diff --git a/dali/python/backend_impl.cc b/dali/python/backend_impl.cc
index b190a09a2dc..21601907113 100644
--- a/dali/python/backend_impl.cc
+++ b/dali/python/backend_impl.cc
@@ -718,6 +718,11 @@ void ExposeTensor(py::module &m) {
       Returns a `TensorGPU` object being a copy of this `TensorCPU`.
       )code",
       py::return_value_policy::take_ownership)
+    .def("as_cpu", [](Tensor<CPUBackend> &t) -> Tensor<CPUBackend>& {
+          return t;
+        },
+      R"code(Bypass, since the object is already an instance of `TensorCPU`.)code",
+      py::return_value_policy::reference_internal)
     .def("copy_to_external",
         [](Tensor<CPUBackend> &t, py::object p) {
           CopyToExternal<mm::memory_kind::host>(ctypes_void_ptr(p), t, AccessOrder::host(), false);
@@ -1174,6 +1179,10 @@ void ExposeTensorList(py::module &m) {
       Returns a `TensorListGPU` object being a copy of this `TensorListCPU`.
       )code",
       py::return_value_policy::take_ownership)
+    .def("as_cpu", [](TensorList<CPUBackend> &t) {
+        return t;
+      }, R"code(Passthrough, as it is already an instance of `TensorListCPU`.)code",
+      py::return_value_policy::internal_reference)
     .def("layout", [](TensorList<CPUBackend> &t) {
       return t.GetLayout().str();
     })
diff --git a/dali/python/nvidia/dali/tensors.pyi b/dali/python/nvidia/dali/tensors.pyi
index 4bfbe7da984..97aadc8d3f0 100644
--- a/dali/python/nvidia/dali/tensors.pyi
+++ b/dali/python/nvidia/dali/tensors.pyi
@@ -27,6 +27,7 @@ class TensorCPU:
     @overload
     def __init__(self, b, layout: str = ..., is_pinned: bool = ...) -> None: ...
     def _as_gpu(self, *args, **kwargs) -> Any: ...
+    def as_cpu(self) -> TensorCPU: ...
 
     # def __dlpack__(self, stream: Optional[int] = None) -> capsule: ...
 
@@ -92,7 +93,8 @@ class TensorListCPU:
     def __init__(self, list_of_tensors: list, layout: str = ...) -> None: ...
 
     # def _as_gpu(self, *args, **kwargs) -> TensorListGPU:  ...
-
+    def as_cpu(self) -> TensorListCPU: ...
+    
     # def as_array(self) -> numpy.ndarray: ...
     def as_array(self) -> Any: ...
     def as_reshaped_tensor(self, arg0: List[int]) -> TensorCPU: ...
diff --git a/dali/test/python/auto_aug/test_augmentations.py b/dali/test/python/auto_aug/test_augmentations.py
index 8499ad30296..1881d87e1b4 100644
--- a/dali/test/python/auto_aug/test_augmentations.py
+++ b/dali/test/python/auto_aug/test_augmentations.py
@@ -19,7 +19,6 @@
 from PIL import Image, ImageEnhance, ImageOps
 from nose2.tools import params, cartesian_params
 
-import nvidia.dali.tensors as _tensors
 from nvidia.dali import fn, pipeline_def
 from nvidia.dali.auto_aug import augmentations as a
 from nvidia.dali.auto_aug.core._utils import get_translations as _get_translations
@@ -91,8 +90,7 @@ def pipeline():
     if dev == "gpu":
         output = output.as_cpu()
     output = [np.array(sample) for sample in output]
-    if isinstance(data, _tensors.TensorListGPU):
-        data = data.as_cpu()
+    data = data.as_cpu()
     data = [np.array(sample) for sample in data]
 
     if modality == "image":
diff --git a/dali/test/python/auto_aug/test_rand_augment.py b/dali/test/python/auto_aug/test_rand_augment.py
index 514abbec350..7df7dec7b68 100644
--- a/dali/test/python/auto_aug/test_rand_augment.py
+++ b/dali/test/python/auto_aug/test_rand_augment.py
@@ -21,7 +21,7 @@
 from scipy.stats import chisquare
 from nose2.tools import params
 
-from nvidia.dali import fn, tensors, types
+from nvidia.dali import fn, types
 from nvidia.dali import pipeline_def
 from nvidia.dali.auto_aug import rand_augment
 from nvidia.dali.auto_aug.core import augmentation
@@ -43,8 +43,7 @@ def debug_discrepancy_helper(*batch_pairs):
     """
 
     def as_array_list(batch):
-        if isinstance(batch, tensors.TensorListGPU):
-            batch = batch.as_cpu()
+        batch = batch.as_cpu()
         return [np.array(sample) for sample in batch]
 
     batch_names = [name for _, _, name in batch_pairs]
diff --git a/dali/test/python/checkpointing/test_dali_stateless_operators.py b/dali/test/python/checkpointing/test_dali_stateless_operators.py
index dccba51af8c..e9ac14504ad 100644
--- a/dali/test/python/checkpointing/test_dali_stateless_operators.py
+++ b/dali/test/python/checkpointing/test_dali_stateless_operators.py
@@ -16,7 +16,6 @@
 import glob
 import numpy as np
 import itertools
-import nvidia.dali as dali
 from nvidia.dali import fn, pipeline_def, types
 from test_utils import (
     compare_pipelines,
@@ -41,9 +40,7 @@
 
 
 def tensor_list_to_array(tensor_list):
-    if isinstance(tensor_list, dali.backend_impl.TensorListGPU):
-        tensor_list = tensor_list.as_cpu()
-    return tensor_list.as_array()
+    return tensor_list.as_cpu().as_array()
 
 
 # Check whether a given pipeline is stateless
diff --git a/dali/test/python/decoder/test_video.py b/dali/test/python/decoder/test_video.py
index d12bd740429..d71614b3776 100644
--- a/dali/test/python/decoder/test_video.py
+++ b/dali/test/python/decoder/test_video.py
@@ -21,7 +21,6 @@
 import os
 from itertools import cycle
 from test_utils import get_dali_extra_path, is_mulit_gpu, skip_if_m60
-from nvidia.dali.backend import TensorListGPU
 from nose2.tools import params
 from nose_utils import SkipTest, attr, assert_raises
 
@@ -77,8 +76,7 @@ def video_decoder_iter(batch_size, epochs=1, device="cpu", module=fn.experimenta
     pipe.build()
     for _ in range(int((epochs * len(files) + batch_size - 1) / batch_size)):
         (output,) = pipe.run()
-        if isinstance(output, TensorListGPU):
-            output = output.as_cpu()
+        output = output.as_cpu()
         for i in range(batch_size):
             yield np.array(output[i])
 
@@ -89,8 +87,7 @@ def ref_iter(epochs=1, device="cpu"):
             pipe = reference_pipeline(filename, device=device)
             pipe.build()
             (output,) = pipe.run()
-            if isinstance(output, TensorListGPU):
-                output = output.as_cpu()
+            output = output.as_cpu()
             yield np.array(output[0])
 
 
diff --git a/dali/test/python/operator_1/test_arithmetic_ops.py b/dali/test/python/operator_1/test_arithmetic_ops.py
index 8f7eb46093d..c6e0d045969 100644
--- a/dali/test/python/operator_1/test_arithmetic_ops.py
+++ b/dali/test/python/operator_1/test_arithmetic_ops.py
@@ -17,7 +17,6 @@
 import nvidia.dali.ops as ops
 import nvidia.dali.types as types
 import nvidia.dali.math as math
-from nvidia.dali.tensors import TensorListGPU
 import numpy as np
 from nose_utils import attr, raises, assert_raises, assert_equals
 from nose2.tools import params
@@ -226,12 +225,6 @@ def default_range(*types):
 ]
 
 
-def as_cpu(tl):
-    if isinstance(tl, TensorListGPU):
-        return tl.as_cpu()
-    return tl
-
-
 def max_dtype(kind, left_dtype, right_dtype):
     return np.dtype(kind + str(max(left_dtype.itemsize, right_dtype.itemsize)))
 
@@ -449,8 +442,8 @@ def get_numpy_input(input, kind, orig_type, target_type):
 
 
 def extract_un_data(pipe_out, sample_id, kind, target_type):
-    input = as_cpu(pipe_out[0]).at(sample_id)
-    out = as_cpu(pipe_out[1]).at(sample_id)
+    input = pipe_out[0].at(sample_id).as_cpu()
+    out = pipe_out[1].at(sample_id).as_cpu()
     assert_equals(out.dtype, target_type)
     in_np = get_numpy_input(input, kind, input.dtype.type, target_type)
     return in_np, out
@@ -465,7 +458,7 @@ def extract_data(pipe_out, sample_id, kinds, target_type):
     arity = len(kinds)
     inputs = []
     for i in range(arity):
-        dali_in = as_cpu(pipe_out[i]).at(sample_id)
+        dali_in = pipe_out[i].at(sample_id).as_cpu()
         numpy_in = get_numpy_input(
             dali_in,
             kinds[i],
@@ -473,7 +466,7 @@ def extract_data(pipe_out, sample_id, kinds, target_type):
             target_type if target_type is not None else dali_in.dtype.type,
         )
         inputs.append(numpy_in)
-    out = as_cpu(pipe_out[arity]).at(sample_id)
+    out = pipe_out[arity].at(sample_id).as_cpu()
     return tuple(inputs) + (out,)
 
 
diff --git a/dali/test/python/operator_1/test_batch_permute.py b/dali/test/python/operator_1/test_batch_permute.py
index a388da06154..8db89eb33e2 100644
--- a/dali/test/python/operator_1/test_batch_permute.py
+++ b/dali/test/python/operator_1/test_batch_permute.py
@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import nvidia.dali as dali
 import nvidia.dali.fn as fn
 from nvidia.dali.pipeline import Pipeline
 import numpy as np
@@ -69,8 +68,7 @@ def _test_permute_batch(device, type):
     for i in range(10):
         orig, permuted, idxs = pipe.run()
         idxs = [int(idxs.at(i)) for i in range(batch_size)]
-        if isinstance(orig, dali.backend.TensorListGPU):
-            orig = orig.as_cpu()
+        orig = orig.as_cpu()
         ref = [orig.at(idx) for idx in idxs]
         check_batch(permuted, ref, len(ref), 0, 0, "abc")
 
@@ -91,10 +89,9 @@ def _test_permute_batch_fixed(device):
     pipe.set_outputs(data, fn.permute_batch(data, indices=idxs))
     pipe.build()
 
-    for i in range(10):
+    for _ in range(10):
         orig, permuted = pipe.run()
-        if isinstance(orig, dali.backend.TensorListGPU):
-            orig = orig.as_cpu()
+        orig = orig.as_cpu()
         ref = [orig.at(idx) for idx in idxs]
         check_batch(permuted, ref, len(ref), 0, 0, "abc")
 
diff --git a/dali/test/python/operator_1/test_coin_flip.py b/dali/test/python/operator_1/test_coin_flip.py
index f5820ec6d2e..d257f67a681 100644
--- a/dali/test/python/operator_1/test_coin_flip.py
+++ b/dali/test/python/operator_1/test_coin_flip.py
@@ -14,7 +14,6 @@
 
 import numpy as np
 import nvidia.dali as dali
-from nvidia.dali.backend_impl import TensorListGPU
 from nvidia.dali.pipeline import Pipeline
 
 
@@ -53,10 +52,10 @@ def shape_gen_f():
         pipe.set_outputs(*outputs)
     pipe.build()
     outputs = pipe.run()
-    data_out = outputs[0].as_cpu() if isinstance(outputs[0], TensorListGPU) else outputs[0]
+    data_out = outputs[0].as_cpu()
     shapes_out = None
     if max_shape is not None:
-        shapes_out = outputs[1].as_cpu() if isinstance(outputs[1], TensorListGPU) else outputs[1]
+        shapes_out = outputs[1].as_cpu()
     p = p if p is not None else 0.5
     for i in range(batch_size):
         data = np.array(data_out[i])
diff --git a/dali/test/python/operator_1/test_coord_flip.py b/dali/test/python/operator_1/test_coord_flip.py
index 45344f9fd1a..b409a6891ad 100644
--- a/dali/test/python/operator_1/test_coord_flip.py
+++ b/dali/test/python/operator_1/test_coord_flip.py
@@ -70,7 +70,7 @@ def check_operator_coord_flip(device, batch_size, layout, shape, center_x, cente
         for sample in range(batch_size):
             in_coords = outputs[0].at(sample)
             if device == "gpu":
-                out_coords = outputs[1].as_cpu().at(sample)
+                out_coords = outputs[1].at(sample).as_cpu()
             else:
                 out_coords = outputs[1].at(sample)
             if in_coords.shape == () or in_coords.shape[0] == 0:
diff --git a/dali/test/python/operator_1/test_crop.py b/dali/test/python/operator_1/test_crop.py
index 0832dce4384..bac1f2becff 100644
--- a/dali/test/python/operator_1/test_crop.py
+++ b/dali/test/python/operator_1/test_crop.py
@@ -16,7 +16,6 @@
 from nvidia.dali import pipeline_def, fn
 import nvidia.dali.ops as ops
 import nvidia.dali.types as types
-import nvidia.dali as dali
 import numpy as np
 import os
 from nose_utils import assert_raises
@@ -580,15 +579,10 @@ def check_crop_with_out_of_bounds_policy_support(
     if fill_values is None:
         fill_values = 0
     pipe.build()
-    for k in range(3):
+    for _ in range(3):
         outs = pipe.run()
-        out = outs[0]
-        in_data = outs[1]
-        if isinstance(out, dali.backend_impl.TensorListGPU):
-            out = out.as_cpu()
-        if isinstance(in_data, dali.backend_impl.TensorListGPU):
-            in_data = in_data.as_cpu()
-
+        out = outs[0].as_cpu()
+        in_data = outs[1].as_cpu()
         assert batch_size == len(out)
         for idx in range(batch_size):
             sample_in = in_data.at(idx)
diff --git a/dali/test/python/operator_1/test_crop_mirror_normalize.py b/dali/test/python/operator_1/test_crop_mirror_normalize.py
index 737216ec7b6..23e5af2018a 100644
--- a/dali/test/python/operator_1/test_crop_mirror_normalize.py
+++ b/dali/test/python/operator_1/test_crop_mirror_normalize.py
@@ -13,7 +13,6 @@
 # limitations under the License.
 
 import numpy as np
-import nvidia.dali as dali
 import nvidia.dali.fn as fn
 import nvidia.dali.ops as ops
 import nvidia.dali.types as types
@@ -773,10 +772,8 @@ def check_cmn_with_out_of_bounds_policy_support(
         out = outs[0]
         in_data = outs[1]
         mirror_data = outs[2]
-        if isinstance(out, dali.backend_impl.TensorListGPU):
-            out = out.as_cpu()
-        if isinstance(in_data, dali.backend_impl.TensorListGPU):
-            in_data = in_data.as_cpu()
+        out = out.as_cpu()
+        in_data = in_data.as_cpu()
 
         assert batch_size == len(out)
         for idx in range(batch_size):
@@ -907,14 +904,7 @@ def pipe():
     for _ in range(3):
         outs = p.run()
         for s in range(batch_size):
-            out, image_like, mean, std = [
-                (
-                    np.array(o[s].as_cpu())
-                    if isinstance(o, dali.backend_impl.TensorListGPU)
-                    else np.array(o[s])
-                )
-                for o in outs
-            ]
+            out, image_like, mean, std = [np.array(o[s].as_cpu()) for o in outs]
         ref_scale = scale or 1.0
         ref_shift = shift or 0.0
         ref_out = ref_scale * (image_like - mean) / std + ref_shift
diff --git a/dali/test/python/operator_1/test_input_promotion.py b/dali/test/python/operator_1/test_input_promotion.py
index d0acb29d64a..ed252988d1c 100644
--- a/dali/test/python/operator_1/test_input_promotion.py
+++ b/dali/test/python/operator_1/test_input_promotion.py
@@ -46,7 +46,7 @@ def test_slice_fn():
     pipe.build()
     o = pipe.run()
     assert np.array_equal(o[0].at(0), np.array([[14], [17]]))
-    assert np.array_equal(o[1].as_cpu().at(0), np.array([[14], [17]]))
+    assert np.array_equal(o[1].at(0).as_cpu(), np.array([[14], [17]]))
 
 
 def test_slice_ops():
@@ -62,7 +62,7 @@ def test_slice_ops():
     pipe.build()
     o = pipe.run()
     assert np.array_equal(o[0].at(0), np.array([[14], [17]]))
-    assert np.array_equal(o[1].as_cpu().at(0), np.array([[14], [17]]))
+    assert np.array_equal(o[1].at(0).as_cpu(), np.array([[14], [17]]))
 
 
 def test_python_function():
diff --git a/dali/test/python/operator_1/test_normal_distribution.py b/dali/test/python/operator_1/test_normal_distribution.py
index c40084b6656..d24ea511486 100644
--- a/dali/test/python/operator_1/test_normal_distribution.py
+++ b/dali/test/python/operator_1/test_normal_distribution.py
@@ -13,7 +13,6 @@
 # limitations under the License.
 
 from nvidia.dali.pipeline import Pipeline
-from nvidia.dali.backend_impl import TensorListGPU
 import nvidia.dali.fn as fn
 import nvidia.dali.types as types
 import numpy as np
@@ -107,10 +106,7 @@ def shape_gen_f():
     pipe.build()
     for i in range(niter):
         outputs = pipe.run()
-        out, shapes, means, stddevs = tuple(
-            outputs[i].as_cpu() if isinstance(outputs[i], TensorListGPU) else outputs[i]
-            for i in range(len(outputs))
-        )
+        out, shapes, means, stddevs = tuple(outputs[i].as_cpu() for i in range(len(outputs)))
         for sample_idx in range(batch_size):
             sample = np.array(out[sample_idx])
             if sample.shape == ():
diff --git a/dali/test/python/operator_1/test_normalize.py b/dali/test/python/operator_1/test_normalize.py
index 13db1283560..76893df7e68 100644
--- a/dali/test/python/operator_1/test_normalize.py
+++ b/dali/test/python/operator_1/test_normalize.py
@@ -13,7 +13,6 @@
 # limitations under the License.
 
 from nvidia.dali.pipeline import Pipeline
-from nvidia.dali import backend
 import nvidia.dali.ops as ops
 import numpy as np
 from test_utils import dali_type
@@ -395,8 +394,7 @@ def iter_setup(self):
 
 
 def to_list(tensor_list):
-    if isinstance(tensor_list, backend.TensorListGPU):
-        tensor_list = tensor_list.as_cpu()
+    tensor_list = tensor_list.as_cpu()
     out = []
     for i in range(len(tensor_list)):
         out.append(tensor_list.at(i))
diff --git a/dali/test/python/operator_1/test_pad.py b/dali/test/python/operator_1/test_pad.py
index a629a9fdc5d..95b24910de7 100644
--- a/dali/test/python/operator_1/test_pad.py
+++ b/dali/test/python/operator_1/test_pad.py
@@ -13,13 +13,11 @@
 # limitations under the License.
 
 import numpy as np
-import nvidia.dali as dali
 import nvidia.dali.fn as fn
 import nvidia.dali.math as math
 import nvidia.dali.ops as ops
 import nvidia.dali.types as types
 from nvidia.dali import Pipeline, pipeline_def
-from nvidia.dali.backend_impl import TensorListGPU
 
 from nose_utils import assert_raises
 from test_utils import (
@@ -104,7 +102,7 @@ def check_pad(device, batch_size, input_max_shape, axes, axis_names, align, shap
     for k in range(5):
         out1, out2 = pipe.run()
 
-        out1_data = out1.as_cpu() if isinstance(out1[0], dali.backend_impl.TensorGPU) else out1
+        out1_data = out1.as_cpu()
         max_shape = [-1] * len(input_max_shape)
 
         for i in range(len(actual_axes)):
@@ -116,7 +114,7 @@ def check_pad(device, batch_size, input_max_shape, axes, axis_names, align, shap
                 if input_shape[dim] > max_shape[dim]:
                     max_shape[dim] = input_shape[dim]
 
-        out2_data = out2.as_cpu() if isinstance(out2[0], dali.backend_impl.TensorGPU) else out2
+        out2_data = out2.as_cpu()
         for i in range(batch_size):
             input_shape = out1_data.at(i).shape
             output_shape = out2_data.at(i).shape
@@ -226,7 +224,7 @@ def check_pad_per_sample_shapes_and_alignment(device="cpu", batch_size=3, ndim=2
         )
     pipe.build()
     for _ in range(num_iter):
-        outs = [out.as_cpu() if isinstance(out, TensorListGPU) else out for out in pipe.run()]
+        outs = [out.as_cpu() for out in pipe.run()]
         for i in range(batch_size):
             in_shape, in_data, req_shape, req_align, out_pad_shape, out_pad_align, out_pad_both = [
                 outs[out_idx].at(i) for out_idx in range(len(outs))
@@ -266,7 +264,7 @@ def check_pad_to_square(device="cpu", batch_size=3, ndim=2, num_iter=3):
         pipe.set_outputs(in_data, out_data)
     pipe.build()
     for _ in range(num_iter):
-        outs = [out.as_cpu() if isinstance(out, TensorListGPU) else out for out in pipe.run()]
+        outs = [out.as_cpu() for out in pipe.run()]
         for i in range(batch_size):
             in_data, out_data = [outs[out_idx].at(i) for out_idx in range(len(outs))]
             in_shape = in_data.shape
diff --git a/dali/test/python/operator_2/test_python_function.py b/dali/test/python/operator_2/test_python_function.py
index ae8214c49c0..0b85f6bbca9 100644
--- a/dali/test/python/operator_2/test_python_function.py
+++ b/dali/test/python/operator_2/test_python_function.py
@@ -320,7 +320,7 @@ def test_python_operator_brightness():
         (numpy_output,) = numpy_brightness.run()
         (dali_output,) = dali_brightness.run()
         for i in range(len(dali_output)):
-            assert numpy.allclose(numpy_output.at(i), dali_output.as_cpu().at(i), rtol=1e-5, atol=1)
+            assert numpy.allclose(numpy_output.at(i), dali_output.at(i).as_cpu(), rtol=1e-5, atol=1)
 
 
 def invalid_function(image):
diff --git a/dali/test/python/operator_2/test_remap.py b/dali/test/python/operator_2/test_remap.py
index 4afd6695fa8..80cd39d4096 100644
--- a/dali/test/python/operator_2/test_remap.py
+++ b/dali/test/python/operator_2/test_remap.py
@@ -14,7 +14,6 @@
 
 import cv2
 import numpy as np
-import nvidia.dali as dali
 import nvidia.dali.fn as fn
 import os.path
 import unittest
@@ -176,16 +175,8 @@ def _compare_pipelines_pixelwise(self, pipe1, pipe2, N_iterations, eps=0.01):
                 f"Numbers of outputs in the pipelines does not match: {len(out1)} vs {len(out2)}.",
             )
             for i in range(len(out1)):
-                out1_data = (
-                    out1[i].as_cpu()
-                    if isinstance(out1[i][0], dali.backend_impl.TensorGPU)
-                    else out1[i]
-                )
-                out2_data = (
-                    out2[i].as_cpu()
-                    if isinstance(out2[i][0], dali.backend_impl.TensorGPU)
-                    else out2[i]
-                )
+                out1_data = out1[i].as_cpu()
+                out2_data = out2[i].as_cpu()
                 for sample1, sample2 in zip(out1_data, out2_data):
                     s1 = np.array(sample1)
                     s2 = np.array(sample2)
diff --git a/dali/test/python/operator_2/test_resize.py b/dali/test/python/operator_2/test_resize.py
index ef1217d6aaa..a8c8099f16f 100644
--- a/dali/test/python/operator_2/test_resize.py
+++ b/dali/test/python/operator_2/test_resize.py
@@ -571,18 +571,14 @@ def get_output():
                 print("Requested output", size[i])
                 assert max_err <= eps
 
-        ref_in = dali_in
-        if isinstance(ref_in, dali.tensors.TensorListGPU):
-            ref_in = ref_in.as_cpu()  # suppress warnings
+        ref_in = dali_in.as_cpu()
         pil_pipe.feed_input("images", ref_in, layout=layout_str(dim, channel_first))
         pil_pipe.feed_input("size", dali_out_size)
         pil_pipe.feed_input("roi_start", roi_start)
         pil_pipe.feed_input("roi_end", roi_end)
         ref = pil_pipe.run()
 
-        dali_resized = o[1]
-        if isinstance(dali_resized, dali.tensors.TensorListGPU):
-            dali_resized = dali_resized.as_cpu()
+        dali_resized = o[1].as_cpu()
         ref_resized = ref[0]
 
         max_avg_err = 0.6 if dim == 3 else 0.4
@@ -874,7 +870,7 @@ def resize_pipe():
     pipe = resize_pipe()
     pipe.build()
     (outs,) = pipe.run()
-    out = outs.as_cpu().at(0)
+    out = outs.at(0).as_cpu()
     global large_data_resized
     if large_data_resized is None:
         large_data_resized = make_cube(350, 224, 224)
diff --git a/dali/test/python/operator_2/test_resize_seq.py b/dali/test/python/operator_2/test_resize_seq.py
index 551ef5867c2..81b20368000 100644
--- a/dali/test/python/operator_2/test_resize_seq.py
+++ b/dali/test/python/operator_2/test_resize_seq.py
@@ -42,7 +42,7 @@ def init_video_data():
 
     video_pipe.build()
     out = video_pipe.run()
-    in_seq = out[0].as_cpu().at(0)
+    in_seq = out[0].at(0).as_cpu()
     return in_seq
 
 
diff --git a/dali/test/python/operator_2/test_subscript.py b/dali/test/python/operator_2/test_subscript.py
index b1b74b05f88..6a0619465c3 100644
--- a/dali/test/python/operator_2/test_subscript.py
+++ b/dali/test/python/operator_2/test_subscript.py
@@ -38,7 +38,7 @@ def test_plain_indexing():
     for i in range(len(inp)):
         x = inp.at(i)
         assert np.array_equal(x[1, 1], cpu.at(i))
-        assert np.array_equal(x[1, 1], gpu.as_cpu().at(i))
+        assert np.array_equal(x[1, 1], gpu.at(i).as_cpu())
 
 
 def _test_indexing(data_gen, input_layout, output_layout, dali_index_func, ref_index_func=None):
@@ -50,7 +50,7 @@ def _test_indexing(data_gen, input_layout, output_layout, dali_index_func, ref_i
         x = inp.at(i)
         ref = (ref_index_func or dali_index_func)(x)
         assert np.array_equal(ref, cpu.at(i))
-        assert np.array_equal(ref, gpu.as_cpu().at(i))
+        assert np.array_equal(ref, gpu.at(i).as_cpu())
         assert cpu.layout() == output_layout
         assert gpu.layout() == output_layout
 
@@ -94,7 +94,7 @@ def test_swapped_ends():
     for i in range(len(inp)):
         x = inp.at(i)
         assert np.array_equal(x[2:1], cpu.at(i))
-        assert np.array_equal(x[2:1], gpu.as_cpu().at(i))
+        assert np.array_equal(x[2:1], gpu.at(i).as_cpu())
 
 
 def test_noop():
@@ -129,7 +129,7 @@ def data_gen():
             j = (j + 1) % len(lo_idxs)
             k = (k + 1) % len(hi_idxs)
             assert np.array_equal(ref, cpu.at(i))
-            assert np.array_equal(ref, gpu.as_cpu().at(i))
+            assert np.array_equal(ref, gpu.at(i).as_cpu())
 
 
 def test_runtime_stride_dim1():
@@ -154,7 +154,7 @@ def data_gen():
             ref = x[::strides[j]]
             # fmt: on
             assert np.array_equal(ref, cpu.at(i))
-            assert np.array_equal(ref, gpu.as_cpu().at(i))
+            assert np.array_equal(ref, gpu.at(i).as_cpu())
             j = (j + 1) % len(strides)
 
 
@@ -180,7 +180,7 @@ def data_gen():
             ref = x[:, ::strides[j]]
             # fmt: on
             assert np.array_equal(ref, cpu.at(i))
-            assert np.array_equal(ref, gpu.as_cpu().at(i))
+            assert np.array_equal(ref, gpu.at(i).as_cpu())
             j = (j + 1) % len(strides)
 
 
@@ -304,7 +304,7 @@ def test_multiple_skipped_dims():
     for i in range(len(inp)):
         x = inp.at(i)
         assert np.array_equal(x[1, :, :, 1], cpu.at(i))
-        assert np.array_equal(x[1, :, :, 1], gpu.as_cpu().at(i))
+        assert np.array_equal(x[1, :, :, 1], gpu.at(i).as_cpu())
 
 
 def test_empty_slice():
@@ -316,4 +316,4 @@ def test_empty_slice():
     for i in range(len(inp)):
         x = inp.at(i)
         assert np.array_equal(x[0:0, 0:1], cpu.at(i))
-        assert np.array_equal(x[0:0, 0:1], gpu.as_cpu().at(i))
+        assert np.array_equal(x[0:0, 0:1], gpu.at(i).as_cpu())
diff --git a/dali/test/python/operator_2/test_uniform.py b/dali/test/python/operator_2/test_uniform.py
index 20c18ab04e5..906bd477dde 100644
--- a/dali/test/python/operator_2/test_uniform.py
+++ b/dali/test/python/operator_2/test_uniform.py
@@ -14,7 +14,6 @@
 
 import nvidia.dali as dali
 from nvidia.dali.pipeline import Pipeline
-from nvidia.dali.backend_impl import TensorListGPU
 import numpy as np
 import scipy.stats as st
 
@@ -27,7 +26,7 @@ def check_uniform_default(device="cpu", batch_size=32, shape=[1e5], val_range=No
     for it in range(niter):
         outputs = pipe.run()
         val_range = (-1.0, 1.0) if val_range is None else val_range
-        data_out = outputs[0].as_cpu() if isinstance(outputs[0], TensorListGPU) else outputs[0]
+        data_out = outputs[0].as_cpu()
         pvs = []
         for i in range(batch_size):
             data = np.array(data_out[i])
@@ -68,7 +67,7 @@ def check_uniform_continuous_next_after(device="cpu", batch_size=32, shape=[1e5]
     pipe.build()
     for it in range(niter):
         outputs = pipe.run()
-        data_out = outputs[0].as_cpu() if isinstance(outputs[0], TensorListGPU) else outputs[0]
+        data_out = outputs[0].as_cpu()
         for i in range(batch_size):
             data = np.array(data_out[i])
             assert (val_range[0] == data).all(), f"{data} is outside of requested range"
@@ -89,7 +88,7 @@ def check_uniform_discrete(device="cpu", batch_size=32, shape=[1e5], values=None
     pipe.build()
     for it in range(niter):
         outputs = pipe.run()
-        data_out = outputs[0].as_cpu() if isinstance(outputs[0], TensorListGPU) else outputs[0]
+        data_out = outputs[0].as_cpu()
         values_set = set(values)
         maxval = np.max(values)
         bins = np.concatenate([values, np.array([np.nextafter(maxval, maxval + 1)])])
diff --git a/dali/test/python/operator_2/test_warp.py b/dali/test/python/operator_2/test_warp.py
index 9355f4a8e52..2f40922880a 100644
--- a/dali/test/python/operator_2/test_warp.py
+++ b/dali/test/python/operator_2/test_warp.py
@@ -299,7 +299,7 @@ def get_data():
     if device == "cpu":
         out = out.at(0)
     else:
-        out = out.as_cpu().at(0)
+        out = out.at(0).as_cpu()
     assert out.shape == (out_size, out_size, channels)
     for c in range(channels):
         assert out[0, 0, c] == c
diff --git a/dali/test/python/sequences_test_utils.py b/dali/test/python/sequences_test_utils.py
index 7074ae5e386..41d9567b985 100644
--- a/dali/test/python/sequences_test_utils.py
+++ b/dali/test/python/sequences_test_utils.py
@@ -21,7 +21,6 @@
 from nvidia.dali import pipeline_def
 import nvidia.dali.fn as fn
 from nvidia.dali import types
-import nvidia.dali.tensors as _Tensors
 
 from test_utils import get_dali_extra_path, check_batch
 
@@ -185,8 +184,7 @@ def arg_data_node(arg_data: ArgData):
 
 
 def as_batch(tensor):
-    if isinstance(tensor, _Tensors.TensorListGPU):
-        tensor = tensor.as_cpu()
+    tensor = tensor.as_cpu()
     return [np.array(sample, dtype=types.to_numpy_type(sample.dtype)) for sample in tensor]
 
 
diff --git a/dali/test/python/test_backend_impl_torch_dlpack.py b/dali/test/python/test_backend_impl_torch_dlpack.py
index 6e918e09ee4..4fa8ab94d6d 100644
--- a/dali/test/python/test_backend_impl_torch_dlpack.py
+++ b/dali/test/python/test_backend_impl_torch_dlpack.py
@@ -23,10 +23,7 @@
 
 def convert_to_torch(tensor, device="cuda", dtype=None, size=None):
     if size is None:
-        if isinstance(tensor, TensorListCPU) or isinstance(tensor, TensorListGPU):
-            t = tensor.as_tensor()
-        else:
-            t = tensor
+        t = tensor.as_tensor()
         size = t.shape()
     dali_torch_tensor = torch.empty(size=size, device=device, dtype=dtype)
     c_type_pointer = ctypes.c_void_p(dali_torch_tensor.data_ptr())
diff --git a/dali/test/python/test_optical_flow.py b/dali/test/python/test_optical_flow.py
index 61b57da3aa2..7a7cea8cf93 100644
--- a/dali/test/python/test_optical_flow.py
+++ b/dali/test/python/test_optical_flow.py
@@ -268,7 +268,7 @@ def check_optflow(output_grid=1, hint_grid=1, use_temporal_hints=False):
         out = pipe.run()
         for i in range(batch_size):
             seq = out[0].at(i)
-            out_field = out[1].as_cpu().at(i)[0]
+            out_field = out[1].at(i).as_cpu()[0]
             _, ref_field = get_mapping(seq.shape[1:3])
             dsize = (out_field.shape[1], out_field.shape[0])
             ref_field = cv2.resize(ref_field, dsize=dsize, interpolation=cv2.INTER_AREA)
diff --git a/dali/test/python/test_pipeline.py b/dali/test/python/test_pipeline.py
index a64f481c15d..010c6c0cdb2 100644
--- a/dali/test/python/test_pipeline.py
+++ b/dali/test/python/test_pipeline.py
@@ -696,7 +696,7 @@ def define_graph(self):
             borderValue=(128, 128, 128),
             flags=(cv2.WARP_INVERSE_MAP + cv2.INTER_LINEAR),
         )
-        dali_output = pipe_out[2].as_cpu().at(i)
+        dali_output = pipe_out[2].at(i).as_cpu()
         maxdif = np.max(cv2.absdiff(out, dali_output) / 255.0)
         assert maxdif < 0.025
 
diff --git a/dali/test/python/test_utils.py b/dali/test/python/test_utils.py
index a8ce3519a8f..55823c4f087 100644
--- a/dali/test/python/test_utils.py
+++ b/dali/test/python/test_utils.py
@@ -14,7 +14,7 @@
 
 import nvidia.dali as dali
 import nvidia.dali.types as dali_types
-from nvidia.dali.backend_impl import TensorListGPU, TensorGPU, TensorListCPU
+from nvidia.dali.backend_impl import TensorListCPU
 from nvidia.dali import plugin_manager
 
 import functools
@@ -275,10 +275,8 @@ def is_error(mean_err, max_err, eps, max_allowed_error):
         return False
 
     import_numpy()
-    if isinstance(batch1, dali.backend_impl.TensorListGPU):
-        batch1 = batch1.as_cpu()
-    if isinstance(batch2, dali.backend_impl.TensorListGPU):
-        batch2 = batch2.as_cpu()
+    batch1 = batch1.as_cpu()
+    batch2 = batch2.as_cpu()
 
     if batch_size is None:
         batch_size = len(batch1)
@@ -384,12 +382,8 @@ def compare_pipelines(
         out2 = pipe2.run()
         assert len(out1) == len(out2)
         for i in range(len(out1)):
-            out1_data = (
-                out1[i].as_cpu() if isinstance(out1[i][0], dali.backend_impl.TensorGPU) else out1[i]
-            )
-            out2_data = (
-                out2[i].as_cpu() if isinstance(out2[i][0], dali.backend_impl.TensorGPU) else out2[i]
-            )
+            out1_data = out1[i].as_cpu()
+            out2_data = out2[i].as_cpu()
             if isinstance(expected_layout, tuple):
                 current_expected_layout = expected_layout[i]
             else:
@@ -541,8 +535,7 @@ def check_output(outputs, ref_out, ref_is_list_of_outputs=None):
     for idx in range(len(outputs)):
         out = outputs[idx]
         ref = ref_out[idx] if ref_is_list_of_outputs else ref_out
-        if isinstance(out, dali.backend_impl.TensorListGPU):
-            out = out.as_cpu()
+        out = out.as_cpu()
         for i in range(len(out)):
             if not np.array_equal(out[i], ref[i]):
                 print("Mismatch at sample", i)
@@ -722,8 +715,7 @@ def update(self, val, n=1):
 
 def to_array(dali_out):
     import_numpy()
-    if isinstance(dali_out, (TensorGPU, TensorListGPU)):
-        dali_out = dali_out.as_cpu()
+    dali_out = dali_out.as_cpu()
     if isinstance(dali_out, TensorListCPU):
         dali_out = dali_out.as_array()
     return np.array(dali_out)
@@ -858,7 +850,7 @@ def gen():
 
 def as_array(tensor):
     import_numpy()
-    return np.array(tensor.as_cpu() if isinstance(tensor, TensorGPU) else tensor)
+    return np.array(tensor.as_cpu())
 
 
 def python_function(*inputs, function, **kwargs):