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:104: in _next_data
    sample[tensor] = bytes_to_text(sample[tensor], "list")
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

buffer = b'\xf6\x80k\xf6\x07\x00', htype = 'list'

    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)
            return arr
        elif htype in ("list", "tag"):
>           lst = json.loads(bytes.decode(buffer), cls=HubJsonDecoder)
E           UnicodeDecodeError: 'utf-8' codec can't decode byte 0xf6 in position 0: invalid start byte

deeplake/core/serialize.py:484: UnicodeDecodeError

request = <SubRequest 'corpus_query_relevances_copy' for <Function test_deepmemory_delete>>
hub_cloud_dev_token = 'eyJhbGciOiJub25lIiwidHlwIjoiSldUIn0.eyJpZCI6InRlc3RpbmdhY2MyIiwiYXBpX2tleSI6IjU4Y0tLb1p6UE1BbThPU2RpbTRiZ2tBekhWekt1VUE3MFJpNTNyZUpKRTJuaiJ9.'

    @pytest.fixture
    def corpus_query_relevances_copy(request, hub_cloud_dev_token):
        if not is_opt_true(request, HUB_CLOUD_OPT):
            pytest.skip(f"{HUB_CLOUD_OPT} flag not set")
            return
    
        corpus = _get_storage_path(request, HUB_CLOUD)
>       query_vs = VectorStore(
            path=f"hub://{HUB_CLOUD_DEV_USERNAME}/deepmemory_test_queries",
            runtime={"tensor_db": True},
            token=hub_cloud_dev_token,
        )

deeplake/tests/path_fixtures.py:482: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
deeplake/core/vectorstore/deeplake_vectorstore.py:120: in __init__
    self.dataset_handler = get_dataset_handler(
deeplake/core/vectorstore/dataset_handlers/dataset_handler.py:13: in get_dataset_handler
    return ClientSideDH(*args, **kwargs)
deeplake/core/vectorstore/dataset_handlers/client_side_dataset_handler.py:66: in __init__
    self.dataset = dataset or dataset_utils.create_or_load_dataset(
deeplake/core/vectorstore/vector_search/dataset/dataset.py:49: in create_or_load_dataset
    return load_dataset(
deeplake/core/vectorstore/vector_search/dataset/dataset.py:99: in load_dataset
    dataset = deeplake.load(
deeplake/util/spinner.py:153: in inner
    return func(*args, **kwargs)
deeplake/api/dataset.py:639: in load
    storage, cache_chain = get_storage_and_cache_chain(
deeplake/util/storage.py:242: in get_storage_and_cache_chain
    storage = storage_provider_from_path(
deeplake/util/storage.py:66: in storage_provider_from_path
    storage = storage_provider_from_hub_path(
deeplake/util/storage.py:159: in storage_provider_from_hub_path
    client = DeepLakeBackendClient(token=token)
deeplake/client/client.py:67: in __init__
    orgs = self.get_user_organizations()
deeplake/client/client.py:355: in get_user_organizations
    response = self.request(
deeplake/client/client.py:148: in request
    check_response_status(response)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

response = <Response [504]>

    def check_response_status(response: requests.Response):
        """Check response status and throw corresponding exception on failure."""
        code = response.status_code
        if code >= 200 and code < 300:
            return
    
        try:
            message = response.json()["description"]
        except Exception:
            message = " "
    
        if code == 400:
            raise BadRequestException(message)
        elif response.status_code == 401:
            raise AuthenticationException
        elif response.status_code == 403:
            raise AuthorizationException(message, response=response)
        elif response.status_code == 404:
            if message != " ":
                raise ResourceNotFoundException(message)
            raise ResourceNotFoundException
        elif response.status_code == 422:
            raise UnprocessableEntityException(message)
        elif response.status_code == 423:
            raise LockedException
        elif response.status_code == 429:
            raise OverLimitException
        elif response.status_code == 502:
            raise BadGatewayException
        elif response.status_code == 504:
>           raise GatewayTimeoutException
E           deeplake.util.exceptions.GatewayTimeoutException: Activeloop server took too long to respond.

deeplake/client/utils.py:74: GatewayTimeoutException

corpus_query_relevances_copy = ('hub://testingacc2/tmp7001_test_deepmemory_test_deepmemory_evaluate', ['0-dimensional biomaterials lack inductive pro...5107', 1]], [['32587939', 1]], ...], 'hub://testingacc2/tmp7001_test_deepmemory_test_deepmemory_evaluate_eval_queries')
questions_embeddings_and_relevances = (array([[-0.01518817,  0.02033963, -0.01228631, ..., -0.00286692,
        -0.0079668 , -0.00414979],
       [-0.003503...A treatment decreases endoplasmic reticulum stress in response to general endoplasmic reticulum stress markers.', ...])
hub_cloud_dev_token = 'eyJhbGciOiJub25lIiwidHlwIjoiSldUIn0.eyJpZCI6InRlc3RpbmdhY2MyIiwiYXBpX2tleSI6IjU4Y0tLb1p6UE1BbThPU2RpbTRiZ2tBekhWekt1VUE3MFJpNTNyZUpKRTJuaiJ9.'

    @pytest.mark.slow
    @pytest.mark.timeout(600)
    @pytest.mark.skipif(sys.platform == "win32", reason="Does not run on Windows")
    @requires_libdeeplake
    def test_deepmemory_evaluate(
        corpus_query_relevances_copy,
        questions_embeddings_and_relevances,
        hub_cloud_dev_token,
    ):
        corpus, _, _, query_path = corpus_query_relevances_copy
        (
            questions_embeddings,
            question_relevances,
            queries,
        ) = questions_embeddings_and_relevances
    
        db = VectorStore(
            corpus,
            runtime={"tensor_db": True},
            token=hub_cloud_dev_token,
        )
    
        # when qvs_params is wrong:
        with pytest.raises(ValueError):
            db.deep_memory.evaluate(
                queries=queries,
                embedding=questions_embeddings,
                relevance=question_relevances,
                qvs_params={
                    "log_queries": True,
                    "branch_name": "wrong_branch",
                },
            )
    
        # embedding_function is not provided in the constructor or in the eval method
        with pytest.raises(ValueError):
            db.deep_memory.evaluate(
                queries=queries,
                relevance=question_relevances,
                qvs_params={
                    "log_queries": True,
                    "branch_name": "wrong_branch",
                },
            )
    
        recall = db.deep_memory.evaluate(
            queries=queries,
            embedding=questions_embeddings,
            relevance=question_relevances,
            qvs_params={
                "branch": "queries",
            },
        )
    
>       assert recall["without model"] == {
            "recall@1": 0.4,
            "recall@3": 0.6,
            "recall@5": 0.6,
            "recall@10": 0.6,
            "recall@50": 0.7,
            "recall@100": 0.9,
        }
E       AssertionError: assert {'recall@1': ...@3': 0.5, ...} == {'recall@1': ...@3': 0.6, ...}
E         Omitting 1 identical items, use -vv to show
E         Differing items:
E         {'recall@3': 0.5} != {'recall@3': 0.6}
E         {'recall@10': 0.5} != {'recall@10': 0.6}
E         {'recall@5': 0.5} != {'recall@5': 0.6}
E         {'recall@50': 0.6} != {'recall@50': 0.7}
E         {'recall@1': 0.3} != {'recall@1': 0.4}
E         Full diff:
E           {
E         -  'recall@1': 0.4,
E         ?                ^
E         +  'recall@1': 0.3,
E         ?                ^
E         -  'recall@10': 0.6,
E         ?                 ^
E         +  'recall@10': 0.5,
E         ?                 ^
E            'recall@100': 0.9,
E         -  'recall@3': 0.6,
E         ?                ^
E         +  'recall@3': 0.5,
E         ?                ^
E         +  'recall@5': 0.5,
E         -  'recall@5': 0.6,
E         +  'recall@50': 0.6,
E         ?           +
E         -  'recall@50': 0.7,
E           }

deeplake/core/vectorstore/deep_memory/test_deepmemory.py:189: AssertionError