local_auth_ds = Dataset(path='./hub_pytest/test_pytorch/test_pytorch_data_decode', tensors=['generic', 'text', 'json', 'list', 'class_label', 'image'])
cat_path = '/home/runner/work/deeplake/deeplake/deeplake/tests/dummy_data/images/cat.jpeg'

    @requires_libdeeplake
    @requires_torch
    @pytest.mark.flaky
    @pytest.mark.slow
    def test_pytorch_data_decode(local_auth_ds, cat_path):
        with local_auth_ds as ds:
            ds.create_tensor("generic")
            for i in range(10):
                ds.generic.append(i)
            ds.create_tensor("text", htype="text")
            for i in range(10):
                ds.text.append(f"hello {i}")
            ds.create_tensor("json", htype="json")
            for i in range(10):
                ds.json.append({"x": i})
            ds.create_tensor("list", htype="list")
            for i in range(10):
                ds.list.append([i, i + 1])
            ds.create_tensor("class_label", htype="class_label")
            animals = [
                "cat",
                "dog",
                "bird",
                "fish",
                "horse",
                "cow",
                "pig",
                "sheep",
                "goat",
                "chicken",
            ]
            ds.class_label.extend(animals)
            ds.create_tensor("image", htype="image", sample_compression="jpeg")
            for i in range(10):
                ds.image.append(deeplake.read(cat_path))
    
        decode_method = {tensor: "data" for tensor in list(ds.tensors.keys())}
        ptds = (
            ds.dataloader()
            .transform(identity)
            .pytorch(decode_method=decode_method, collate_fn=identity_collate)
        )
>       for i, batch in enumerate(ptds):

deeplake/enterprise/test_pytorch.py:948: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
deeplake/enterprise/dataloader.py:881: in __next__
    return next(self._iterator)
/opt/hostedtoolcache/Python/3.10.14/x64/lib/python3.10/site-packages/indra/pytorch/loader.py:156: in __next__
    return next(self._iterator)
/opt/hostedtoolcache/Python/3.10.14/x64/lib/python3.10/site-packages/indra/pytorch/single_process_iterator.py:80: in __next__
    return self.get_data()
/opt/hostedtoolcache/Python/3.10.14/x64/lib/python3.10/site-packages/indra/pytorch/single_process_iterator.py:117: in get_data
    batch = self._next_data()
/opt/hostedtoolcache/Python/3.10.14/x64/lib/python3.10/site-packages/indra/pytorch/single_process_iterator.py:102: in _next_data
    sample[tensor] = bytes_to_text(sample[tensor], "json")
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

buffer = b'\x12YT\xb1\xd2\x7f\x00\x00', htype = 'json'

    def bytes_to_text(buffer, htype):
        buffer = bytes(buffer)
        if htype == "json":
            arr = np.empty(1, dtype=object)
>           arr[0] = json.loads(bytes.decode(buffer), cls=HubJsonDecoder)
E           UnicodeDecodeError: 'utf-8' codec can't decode byte 0xb1 in position 3: invalid start byte

deeplake/core/serialize.py:481: UnicodeDecodeError

local_auth_ds = Dataset(path='./hub_pytest/test_tensorflow/test_groups', tensors=['images/jpegs/cats', 'images/pngs/flowers'])
compressed_image_paths = {'bmp': ['/home/runner/work/deeplake/deeplake/deeplake/tests/dummy_data/images/car.bmp'], 'dib': ['/home/runner/work/d...ata/images/hopper.fli'], 'gif': ['/home/runner/work/deeplake/deeplake/deeplake/tests/dummy_data/images/boat.gif'], ...}

    @requires_tensorflow
    @requires_libdeeplake
    @pytest.mark.slow
    @pytest.mark.flaky
    def test_groups(local_auth_ds, compressed_image_paths):
        img1 = deeplake.read(compressed_image_paths["jpeg"][0])
        img2 = deeplake.read(compressed_image_paths["png"][0])
        with local_auth_ds as ds:
            ds.create_tensor("images/jpegs/cats", htype="image", sample_compression="jpeg")
            ds.create_tensor("images/pngs/flowers", htype="image", sample_compression="png")
            for _ in range(10):
                ds.images.jpegs.cats.append(img1)
                ds.images.pngs.flowers.append(img2)
    
        another_ds = deeplake.dataset(
            ds.path,
            token=ds.token,
        )
        dl = another_ds.dataloader().tensorflow(return_index=False)
        for i, (cat, flower) in enumerate(dl):
            assert cat[0].shape == another_ds.images.jpegs.cats[i].numpy().shape
            assert flower[0].shape == another_ds.images.pngs.flowers[i].numpy().shape
    
        dl = another_ds.images.dataloader().tensorflow(return_index=False)
        for sample in dl:
            cat = sample["images/jpegs/cats"]
            flower = sample["images/pngs/flowers"]
>           np.testing.assert_array_equal(cat[0], img1.array)

deeplake/enterprise/test_tensorflow.py:340: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

args = (<built-in function eq>, <tf.Tensor: shape=(900, 900, 3), dtype=uint8, numpy=
array([[[128, 127, 133],
        [128, 1...],
        [87, 71, 58],
        ...,
        [19, 20, 22],
        [19, 20, 22],
        [19, 20, 22]]], dtype=uint8))
kwds = {'err_msg': '', 'header': 'Arrays are not equal', 'strict': False, 'verbose': True}

    @wraps(func)
    def inner(*args, **kwds):
        with self._recreate_cm():
>           return func(*args, **kwds)
E           AssertionError: 
E           Arrays are not equal
E           
E           Mismatched elements: 2423881 / 2430000 (99.7%)
E           Max absolute difference: 128
E           Max relative difference: 127.
E            x: array([[[128, 127, 133],
E                   [128, 127, 133],
E                   [129, 128, 133],...
E            y: array([[[40, 41, 45],
E                   [38, 39, 43],
E                   [36, 37, 41],...

/opt/hostedtoolcache/Python/3.10.14/x64/lib/python3.10/contextlib.py:79: AssertionError