version = '3.8.23', request = <FixtureRequest for <Function test[3.8.23]>>

    @versions
    def test(version, request):
        assert_version(version)
>       ds = load_dataset(version)

buH/buh/tests/test_read.py:17: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
buH/buh/tests/common.py:86: in load_dataset
    return _bc_load_dataset(dataset_path)
buH/buh/tests/common.py:79: in _bc_load_dataset
    return loader(path)
buH/buh/tests/common.py:58: in _load1
    return hub.load(path)
deeplake/util/spinner.py:151: in inner
    return func(*args, **kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

path = './datasets/3_8_23', read_only = None, memory_cache_size = 2000
local_cache_size = 0, creds = {}, token = None, org_id = None, verbose = True
access_method = 'stream', unlink = False, reset = False, check_integrity = None
lock_timeout = 0, lock_enabled = True, index_params = None

    @staticmethod
    @spinner
    def load(
        path: Union[str, pathlib.Path],
        read_only: Optional[bool] = None,
        memory_cache_size: int = DEFAULT_MEMORY_CACHE_SIZE,
        local_cache_size: int = DEFAULT_LOCAL_CACHE_SIZE,
        creds: Optional[Union[dict, str]] = None,
        token: Optional[str] = None,
        org_id: Optional[str] = None,
        verbose: bool = True,
        access_method: str = "stream",
        unlink: bool = False,
        reset: bool = False,
        check_integrity: Optional[bool] = None,
        lock_timeout: Optional[int] = 0,
        lock_enabled: Optional[bool] = True,
        index_params: Optional[Dict[str, Union[int, str]]] = None,
    ) -> Dataset:
        """Loads an existing dataset
    
        Examples:
    
            >>> ds = deeplake.load("hub://username/dataset")
            >>> ds = deeplake.load("s3://mybucket/my_dataset")
            >>> ds = deeplake.load("./datasets/my_dataset", overwrite=True)
    
            Loading to a specfic version:
    
            >>> ds = deeplake.load("hub://username/dataset@new_branch")
            >>> ds = deeplake.load("hub://username/dataset@3e49cded62b6b335c74ff07e97f8451a37aca7b2)
    
            >>> my_commit_id = "3e49cded62b6b335c74ff07e97f8451a37aca7b2"
            >>> ds = deeplake.load(f"hub://username/dataset@{my_commit_id}")
    
        Args:
            path (str, pathlib.Path): - The full path to the dataset. Can be:
                - a Deep Lake cloud path of the form ``hub://username/datasetname``. To write to Deep Lake cloud datasets, ensure that you are authenticated to Deep Lake (pass in a token using the 'token' parameter).
                - an s3 path of the form ``s3://bucketname/path/to/dataset``. Credentials are required in either the environment or passed to the creds argument.
                - a local file system path of the form ``./path/to/dataset`` or ``~/path/to/dataset`` or ``path/to/dataset``.
                - a memory path of the form ``mem://path/to/dataset`` which doesn't save the dataset but keeps it in memory instead. Should be used only for testing as it does not persist.
                - Loading to a specific version:
    
                        - You can also specify a ``commit_id`` or ``branch`` to load the dataset to that version directly by using the ``@`` symbol.
                        - The path will then be of the form ``hub://username/dataset@{branch}`` or ``hub://username/dataset@{commit_id}``.
                        - See examples above.
            read_only (bool, optional): Opens dataset in read only mode if this is passed as ``True``. Defaults to ``False``.
                Datasets stored on Deep Lake cloud that your account does not have write access to will automatically open in read mode.
            memory_cache_size (int): The size of the memory cache to be used in MB.
            local_cache_size (int): The size of the local filesystem cache to be used in MB.
            creds (dict, str, optional): The string ``ENV`` or a dictionary containing credentials used to access the dataset at the path.
                - If 'aws_access_key_id', 'aws_secret_access_key', 'aws_session_token' are present, these take precedence over credentials present in the environment or in credentials file. Currently only works with s3 paths.
                - It supports 'aws_access_key_id', 'aws_secret_access_key', 'aws_session_token', 'endpoint_url', 'aws_region', 'profile_name' as keys.
                - If 'ENV' is passed, credentials are fetched from the environment variables. This is also the case when creds is not passed for cloud datasets. For datasets connected to hub cloud, specifying 'ENV' will override the credentials fetched from Activeloop and use local ones.
            token (str, optional): Activeloop token, used for fetching credentials to the dataset at path if it is a Deep Lake dataset. This is optional, tokens are normally autogenerated.
            org_id (str, Optional): Organization id to be used for enabling high-performance features. Only applicable for local datasets.
            verbose (bool): If ``True``, logs will be printed. Defaults to ``True``.
            access_method (str): The access method to use for the dataset. Can be:
    
                    - 'stream'
    
                        - Streams the data from the dataset i.e. only fetches data when required. This is the default value.
    
                    - 'download'
    
                        - Downloads the data to the local filesystem to the path specified in environment variable ``DEEPLAKE_DOWNLOAD_PATH``.
                          This will overwrite ``DEEPLAKE_DOWNLOAD_PATH``.
                        - Raises an exception if ``DEEPLAKE_DOWNLOAD_PATH`` environment variable is not set or if the dataset does not exist.
                        - The 'download' access method can be modified to specify num_workers and/or scheduler.
                          For example: 'download:2:processed' will use 2 workers and use processed scheduler, while 'download:3' will use 3 workers and
                          default scheduler (threaded), and 'download:processed' will use a single worker and use processed scheduler.
    
                    - 'local'
    
                        - Downloads the dataset if it doesn't already exist, otherwise loads from local storage.
                        - Raises an exception if ``DEEPLAKE_DOWNLOAD_PATH`` environment variable is not set.
                        - The 'local' access method can be modified to specify num_workers and/or scheduler to be used in case dataset needs to be downloaded.
                          If dataset needs to be downloaded, 'local:2:processed' will use 2 workers and use processed scheduler, while 'local:3' will use 3 workers
                          and default scheduler (threaded), and 'local:processed' will use a single worker and use processed scheduler.
            unlink (bool): Downloads linked samples if set to ``True``. Only applicable if ``access_method`` is ``download`` or ``local``. Defaults to ``False``.
            reset (bool): If the specified dataset cannot be loaded due to a corrupted HEAD state of the branch being loaded,
                          setting ``reset=True`` will reset HEAD changes and load the previous version.
            check_integrity (bool, Optional): Performs an integrity check by default (None) if the dataset has 20 or fewer tensors.
                                              Set to ``True`` to force integrity check, ``False`` to skip integrity check.
    
        ..
            # noqa: DAR101
    
        Returns:
            Dataset: Dataset loaded using the arguments provided.
    
        Raises:
            DatasetHandlerError: If a Dataset does not exist at the given path.
            AgreementError: When agreement is rejected
            UserNotLoggedInException: When user is not authenticated
            InvalidTokenException: If the specified toke is invalid
            TokenPermissionError: When there are permission or other errors related to token
            CheckoutError: If version address specified in the path cannot be found
            DatasetCorruptError: If loading the dataset failed due to corruption and ``reset`` is not ``True``
            ReadOnlyModeError: If reset is attempted in read-only mode
            LockedException: When attempting to open a dataset for writing when it is locked by another machine
            ValueError: If ``org_id`` is specified for a non-local dataset
            Exception: Re-raises caught exception if reset cannot fix the issue
            ValueError: If the org id is provided but the dataset is not local
    
        Warning:
            Setting ``access_method`` to download will overwrite the local copy of the dataset if it was previously downloaded.
    
        Note:
            Any changes made to the dataset in download / local mode will only be made to the local copy and will not be reflected in the original dataset.
        """
        access_method, num_workers, scheduler = parse_access_method(access_method)
        check_access_method(access_method, overwrite=False, unlink=unlink)
    
        path, address = process_dataset_path(path)
    
        if creds is None:
            creds = {}
    
        if org_id is not None and get_path_type(path) != "local":
            raise ValueError("org_id parameter can only be used with local datasets")
    
        try:
            storage, cache_chain = get_storage_and_cache_chain(
                path=path,
                read_only=read_only,
                creds=creds,
                token=token,
                memory_cache_size=memory_cache_size,
                local_cache_size=local_cache_size,
            )
            feature_report_path(
                path,
                "load",
                {
                    "lock_enabled": lock_enabled,
                    "lock_timeout": lock_timeout,
                    "index_params": index_params,
                },
                token=token,
            )
        except Exception as e:
            if isinstance(e, UserNotLoggedInException):
                raise UserNotLoggedInException from None
            raise
        if not dataset_exists(cache_chain):
>           raise DatasetHandlerError(
                f"A Deep Lake dataset does not exist at the given path ({path}). Check the path provided or in case you want to create a new dataset, use deeplake.empty()."
            )
E           deeplake.util.exceptions.DatasetHandlerError: A Deep Lake dataset does not exist at the given path (./datasets/3_8_23). Check the path provided or in case you want to create a new dataset, use deeplake.empty().

deeplake/api/dataset.py:644: DatasetHandlerError

version = '3.8.23'
request = <FixtureRequest for <Function test_new_samples[3.8.23]>>

    @versions
    def test_new_samples(version, request):
        assert_version(version)
>       ds = load_dataset_copy(version, overwrite=True)

buH/buh/tests/test_write.py:25: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
buH/buh/tests/common.py:96: in load_dataset_copy
    new_path = shutil.copytree(dataset_path, new_dataset_path)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

src = './datasets/3_8_23', dst = './datasets/3_8_23_ffw3_8_23', symlinks = False
ignore = None, copy_function = <function copy2 at 0x7f1fa4923d00>
ignore_dangling_symlinks = False, dirs_exist_ok = False

    def copytree(src, dst, symlinks=False, ignore=None, copy_function=copy2,
                 ignore_dangling_symlinks=False, dirs_exist_ok=False):
        """Recursively copy a directory tree and return the destination directory.
    
        If exception(s) occur, an Error is raised with a list of reasons.
    
        If the optional symlinks flag is true, symbolic links in the
        source tree result in symbolic links in the destination tree; if
        it is false, the contents of the files pointed to by symbolic
        links are copied. If the file pointed by the symlink doesn't
        exist, an exception will be added in the list of errors raised in
        an Error exception at the end of the copy process.
    
        You can set the optional ignore_dangling_symlinks flag to true if you
        want to silence this exception. Notice that this has no effect on
        platforms that don't support os.symlink.
    
        The optional ignore argument is a callable. If given, it
        is called with the `src` parameter, which is the directory
        being visited by copytree(), and `names` which is the list of
        `src` contents, as returned by os.listdir():
    
            callable(src, names) -> ignored_names
    
        Since copytree() is called recursively, the callable will be
        called once for each directory that is copied. It returns a
        list of names relative to the `src` directory that should
        not be copied.
    
        The optional copy_function argument is a callable that will be used
        to copy each file. It will be called with the source path and the
        destination path as arguments. By default, copy2() is used, but any
        function that supports the same signature (like copy()) can be used.
    
        If dirs_exist_ok is false (the default) and `dst` already exists, a
        `FileExistsError` is raised. If `dirs_exist_ok` is true, the copying
        operation will continue if it encounters existing directories, and files
        within the `dst` tree will be overwritten by corresponding files from the
        `src` tree.
        """
        sys.audit("shutil.copytree", src, dst)
>       with os.scandir(src) as itr:
E       FileNotFoundError: [Errno 2] No such file or directory: './datasets/3_8_23'

/opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/shutil.py:557: FileNotFoundError

version = '3.8.23'
request = <FixtureRequest for <Function test_new_tensor[3.8.23]>>

    @versions
    def test_new_tensor(version, request):
        assert_version(version)
>       ds = load_dataset_copy(version, overwrite=True)

buH/buh/tests/test_write.py:53: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
buH/buh/tests/common.py:96: in load_dataset_copy
    new_path = shutil.copytree(dataset_path, new_dataset_path)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

src = './datasets/3_8_23', dst = './datasets/3_8_23_ffw3_8_23', symlinks = False
ignore = None, copy_function = <function copy2 at 0x7f1fa4923d00>
ignore_dangling_symlinks = False, dirs_exist_ok = False

    def copytree(src, dst, symlinks=False, ignore=None, copy_function=copy2,
                 ignore_dangling_symlinks=False, dirs_exist_ok=False):
        """Recursively copy a directory tree and return the destination directory.
    
        If exception(s) occur, an Error is raised with a list of reasons.
    
        If the optional symlinks flag is true, symbolic links in the
        source tree result in symbolic links in the destination tree; if
        it is false, the contents of the files pointed to by symbolic
        links are copied. If the file pointed by the symlink doesn't
        exist, an exception will be added in the list of errors raised in
        an Error exception at the end of the copy process.
    
        You can set the optional ignore_dangling_symlinks flag to true if you
        want to silence this exception. Notice that this has no effect on
        platforms that don't support os.symlink.
    
        The optional ignore argument is a callable. If given, it
        is called with the `src` parameter, which is the directory
        being visited by copytree(), and `names` which is the list of
        `src` contents, as returned by os.listdir():
    
            callable(src, names) -> ignored_names
    
        Since copytree() is called recursively, the callable will be
        called once for each directory that is copied. It returns a
        list of names relative to the `src` directory that should
        not be copied.
    
        The optional copy_function argument is a callable that will be used
        to copy each file. It will be called with the source path and the
        destination path as arguments. By default, copy2() is used, but any
        function that supports the same signature (like copy()) can be used.
    
        If dirs_exist_ok is false (the default) and `dst` already exists, a
        `FileExistsError` is raised. If `dirs_exist_ok` is true, the copying
        operation will continue if it encounters existing directories, and files
        within the `dst` tree will be overwritten by corresponding files from the
        `src` tree.
        """
        sys.audit("shutil.copytree", src, dst)
>       with os.scandir(src) as itr:
E       FileNotFoundError: [Errno 2] No such file or directory: './datasets/3_8_23'

/opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/shutil.py:557: FileNotFoundError

version = '3.8.23'
request = <FixtureRequest for <Function test_update_samples[3.8.23]>>

    @versions
    def test_update_samples(version, request):
        assert_version(version)
>       ds = load_dataset_copy(version, overwrite=True)

buH/buh/tests/test_write.py:64: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
buH/buh/tests/common.py:96: in load_dataset_copy
    new_path = shutil.copytree(dataset_path, new_dataset_path)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

src = './datasets/3_8_23', dst = './datasets/3_8_23_ffw3_8_23', symlinks = False
ignore = None, copy_function = <function copy2 at 0x7f1fa4923d00>
ignore_dangling_symlinks = False, dirs_exist_ok = False

    def copytree(src, dst, symlinks=False, ignore=None, copy_function=copy2,
                 ignore_dangling_symlinks=False, dirs_exist_ok=False):
        """Recursively copy a directory tree and return the destination directory.
    
        If exception(s) occur, an Error is raised with a list of reasons.
    
        If the optional symlinks flag is true, symbolic links in the
        source tree result in symbolic links in the destination tree; if
        it is false, the contents of the files pointed to by symbolic
        links are copied. If the file pointed by the symlink doesn't
        exist, an exception will be added in the list of errors raised in
        an Error exception at the end of the copy process.
    
        You can set the optional ignore_dangling_symlinks flag to true if you
        want to silence this exception. Notice that this has no effect on
        platforms that don't support os.symlink.
    
        The optional ignore argument is a callable. If given, it
        is called with the `src` parameter, which is the directory
        being visited by copytree(), and `names` which is the list of
        `src` contents, as returned by os.listdir():
    
            callable(src, names) -> ignored_names
    
        Since copytree() is called recursively, the callable will be
        called once for each directory that is copied. It returns a
        list of names relative to the `src` directory that should
        not be copied.
    
        The optional copy_function argument is a callable that will be used
        to copy each file. It will be called with the source path and the
        destination path as arguments. By default, copy2() is used, but any
        function that supports the same signature (like copy()) can be used.
    
        If dirs_exist_ok is false (the default) and `dst` already exists, a
        `FileExistsError` is raised. If `dirs_exist_ok` is true, the copying
        operation will continue if it encounters existing directories, and files
        within the `dst` tree will be overwritten by corresponding files from the
        `src` tree.
        """
        sys.audit("shutil.copytree", src, dst)
>       with os.scandir(src) as itr:
E       FileNotFoundError: [Errno 2] No such file or directory: './datasets/3_8_23'

/opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/shutil.py:557: FileNotFoundError

local_path = './hub_pytest/test_deeplake_vectorstore/test_returning_tql_for_exec_option_compute_engine_should_return_correct_tql'
hub_cloud_dev_token = 'eyJhbGciOiJub25lIiwidHlwIjoiSldUIn0.eyJpZCI6InRlc3RpbmdhY2MyIiwiYXBpX2tleSI6IjU4Y0tLb1p6UE1BbThPU2RpbTRiZ2tBekhWekt1VUE3MFJpNTNyZUpKRTJuaiJ9.'

    def test_returning_tql_for_exec_option_compute_engine_should_return_correct_tql(
        local_path,
        hub_cloud_dev_token,
    ):
        db = VectorStore(
            path=local_path,
            token=hub_cloud_dev_token,
        )
    
        texts, embeddings, ids, metadatas, _ = utils.create_data(
            number_of_data=10, embedding_dim=3
        )
    
        db.add(text=texts, embedding=embeddings, id=ids, metadata=metadatas)
    
        query_embedding = np.zeros(3, dtype=np.float32)
>       output = db.search(embedding=query_embedding, return_tql=True)

deeplake\core\vectorstore\test_deeplake_vectorstore.py:2921: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
deeplake\core\vectorstore\deeplake_vectorstore.py:316: in search
    return self.dataset_handler.search(
deeplake\core\vectorstore\deep_memory\deep_memory.py:59: in wrapper
    return func(self, *args, **kwargs)
deeplake\core\vectorstore\dataset_handlers\client_side_dataset_handler.py:235: in search
    return vector_search.search(
deeplake\core\vectorstore\vector_search\vector_search.py:55: in search
    return EXEC_OPTION_TO_SEARCH_TYPE[exec_option](
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

query = None, query_emb = array([0., 0., 0.], dtype=float32)
exec_option = 'python'
dataset = Dataset(path='./hub_pytest/test_deeplake_vectorstore/test_returning_tql_for_exec_option_compute_engine_should_return_correct_tql', tensors=['text', 'metadata', 'embedding', 'id'])
logger = <Logger deeplake.core.vectorstore.deeplake_vectorstore (INFO)>
filter = None, embedding_tensor = 'embedding', distance_metric = 'COS', k = 4
return_tensors = ['text', 'metadata', 'id'], return_view = False
token = 'eyJhbGciOiJub25lIiwidHlwIjoiSldUIn0.eyJpZCI6InRlc3RpbmdhY2MyIiwiYXBpX2tleSI6IjU4Y0tLb1p6UE1BbThPU2RpbTRiZ2tBekhWekt1VUE3MFJpNTNyZUpKRTJuaiJ9.'
org_id = None, return_tql = True

    def vector_search(
        query,
        query_emb,
        exec_option,
        dataset,
        logger,
        filter,
        embedding_tensor,
        distance_metric,
        k,
        return_tensors,
        return_view,
        token,
        org_id,
        return_tql,
    ) -> Union[Dict, DeepLakeDataset]:
        if query is not None:
            raise NotImplementedError(
                f"User-specified TQL queries are not supported for exec_option={exec_option} "
            )
    
        if return_tql:
>           raise NotImplementedError(
                f"return_tql is not supported for exec_option={exec_option}"
            )
E           NotImplementedError: return_tql is not supported for exec_option=python

deeplake\core\vectorstore\vector_search\python\vector_search.py:31: NotImplementedError

self = Sample(is_lazy=True, path=https://picsum.photos/10/10)

    def _read_from_path(self) -> bytes:  # type: ignore
        if self._buffer is None:
            path_type = get_path_type(self.path)
            try:
                if path_type == "local":
                    self._buffer = self._read_from_local()
                elif path_type == "gcs":
                    self._buffer = self._read_from_gcs()
                elif path_type == "s3":
                    self._buffer = self._read_from_s3()
                elif path_type == "azure":
                    self._buffer = self._read_from_azure()
                elif path_type == "gdrive":
                    self._buffer = self._read_from_gdrive()
                elif path_type == "http":
>                   self._buffer = self._read_from_http(timeout=self._timeout)

deeplake/core/sample.py:454: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Sample(is_lazy=True, path=https://picsum.photos/10/10), timeout = None

    def _read_from_http(self, timeout=None) -> bytes:
        assert self.path is not None
        if "Authorization" in self._creds:
            headers = {"Authorization": self._creds["Authorization"]}
        else:
            headers = {}
        result = requests.get(self.path, headers=headers, timeout=timeout)
        if result.status_code != 200:
>           raise UnableToReadFromUrlError(self.path, result.status_code)
E           deeplake.util.exceptions.UnableToReadFromUrlError: Unable to read from url https://picsum.photos/10/10. Status code: 520

deeplake/core/sample.py:520: UnableToReadFromUrlError

The above exception was the direct cause of the following exception:

self = <deeplake.core.transform.transform_tensor.TransformTensor object at 0x7fdca34fee10>
item = Sample(is_lazy=True, path=https://picsum.photos/10/10)

    def append(self, item):
        """Adds an item to the tensor."""
        if self.is_group:
            raise TensorDoesNotExistError(self.name)
        try:
            # optimization applicable only if extending
            self.non_numpy_only()
    
>           self._verify_item(item)

deeplake/core/transform/transform_tensor.py:122: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
deeplake/core/transform/transform_tensor.py:112: in _verify_item
    shape = getattr(item, "shape", None)  # verify sample
deeplake/core/sample.py:166: in shape
    self._read_meta()
deeplake/core/sample.py:201: in _read_meta
    f = self._read_from_path()
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = Sample(is_lazy=True, path=https://picsum.photos/10/10)

    def _read_from_path(self) -> bytes:  # type: ignore
        if self._buffer is None:
            path_type = get_path_type(self.path)
            try:
                if path_type == "local":
                    self._buffer = self._read_from_local()
                elif path_type == "gcs":
                    self._buffer = self._read_from_gcs()
                elif path_type == "s3":
                    self._buffer = self._read_from_s3()
                elif path_type == "azure":
                    self._buffer = self._read_from_azure()
                elif path_type == "gdrive":
                    self._buffer = self._read_from_gdrive()
                elif path_type == "http":
                    self._buffer = self._read_from_http(timeout=self._timeout)
            except Exception as e:
>               raise SampleReadError(self.path) from e  # type: ignore
E               deeplake.util.exceptions.SampleReadError: Unable to read sample from https://picsum.photos/10/10

deeplake/core/sample.py:456: SampleReadError

The above exception was the direct cause of the following exception:

data_slice = Dataset(path='./hub_pytest/test_query/test_link_materialize-1-', index=Index([slice(0, 20, 2)]), tensors=['abc'])
offset = 0
transform_dataset = <deeplake.core.transform.transform_dataset.TransformDataset object at 0x7fdc9000a190>
pipeline = <deeplake.core.transform.transform.Pipeline object at 0x7fdca392c050>
tensors = ['abc'], skip_ok = True
pg_callback = <function ComputeProvider.map_with_progress_bar.<locals>.sub_func.<locals>.pg_callback at 0x7fdd92f1b420>
ignore_errors = False

    def _transform_and_append_data_slice(
        data_slice,
        offset,
        transform_dataset,
        pipeline,
        tensors,
        skip_ok,
        pg_callback,
        ignore_errors,
    ):
        """Appends a data slice. Returns ``True`` if any samples were appended and ``False`` otherwise."""
        try:
            import pandas as pd  # type: ignore
        except ImportError:
            pd = None
    
        n = len(data_slice)
        skipped_samples = 0
        skipped_samples_in_current_batch = 0
    
        pipeline_checked = False
    
        last_pg_update_time = time.time()
        progress = 0
    
        for i, sample in enumerate(
            (data_slice[i : i + 1] for i in range(n))
            if pd and isinstance(data_slice, pd.DataFrame)
            else data_slice
        ):
            try:
                transform_dataset.set_start_input_idx(i)
    
                try:
>                   out = transform_sample(sample, pipeline, tensors)

deeplake/util/transform.py:227: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
deeplake/util/transform.py:76: in transform_sample
    fn(out, result, *args, **kwargs)
deeplake/core/dataset/dataset.py:4203: in _copy_tensor_append
    sample_out[tensor_name].append(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <deeplake.core.transform.transform_tensor.TransformTensor object at 0x7fdca34fee10>
item = Sample(is_lazy=True, path=https://picsum.photos/10/10)

    def append(self, item):
        """Adds an item to the tensor."""
        if self.is_group:
            raise TensorDoesNotExistError(self.name)
        try:
            # optimization applicable only if extending
            self.non_numpy_only()
    
            self._verify_item(item)
            self.items.append(item)
            self._item_added(item)
        except Exception as e:
            self.items.clear()
>           raise SampleAppendError(self.name, item) from e
E           deeplake.util.exceptions.SampleAppendError: Failed to append the sample at path 'https://picsum.photos/10/10' to the tensor 'abc'. See more details in the traceback.

deeplake/core/transform/transform_tensor.py:127: SampleAppendError

The above exception was the direct cause of the following exception:

local_ds = Dataset(path='./hub_pytest/test_query/test_link_materialize-1-', tensors=['abc'])
num_workers = 1

    @pytest.mark.slow
    @pytest.mark.parametrize("num_workers", [1, 2])
    def test_link_materialize(local_ds, num_workers):
        with local_ds as ds:
            ds.create_tensor("abc", htype="link[image]", sample_compression="jpg")
            ds.abc.extend(
                [
                    (
                        deeplake.link("https://picsum.photos/20/20")
                        if i % 2
                        else deeplake.link("https://picsum.photos/10/10")
                    )
                    for i in range(20)
                ]
            )
            ds.commit()
    
        view = ds[::2]
>       view.save_view(id="view_1", optimize=True, num_workers=num_workers)

deeplake/core/query/test/test_query.py:480: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
deeplake/core/dataset/dataset.py:3692: in save_view
    return self._save_view(
deeplake/core/dataset/dataset.py:3790: in _save_view
    vds = self._save_view_in_subdir(
deeplake/core/dataset/dataset.py:3590: in _save_view_in_subdir
    self._write_vds(vds, info, copy, tensors, num_workers, scheduler, ignore_errors)
deeplake/core/dataset/dataset.py:3521: in _write_vds
    self._copy(
deeplake/core/dataset/dataset.py:4222: in _copy
    )().eval(
deeplake/core/transform/transform.py:105: in eval
    pipeline.eval(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <deeplake.core.transform.transform.Pipeline object at 0x7fdca392c050>
data_in = Dataset(path='./hub_pytest/test_query/test_link_materialize-1-', index=Index([slice(None, None, 2)]), tensors=['abc'])
ds_out = Dataset(path='./hub_pytest/test_query/test_link_materialize-1-/.queries/view_1', tensors=['abc'])
num_workers = 1, scheduler = 'threaded', progressbar = True, skip_ok = True
check_lengths = False, pad_data_in = False, read_only_ok = False
cache_size = 16, checkpoint_interval = 0, ignore_errors = False, verbose = True
kwargs = {'disable_label_sync': True, 'extend_only': False}, overwrite = False
original_data_in = Dataset(path='./hub_pytest/test_query/test_link_materialize-1-', index=Index([slice(None, None, 2)]), tensors=['abc'])
initial_padding_state = None
target_ds = Dataset(path='./hub_pytest/test_query/test_link_materialize-1-/.queries/view_1', tensors=['abc'])
compute_provider = <deeplake.core.compute.thread.ThreadProvider object at 0x7fdcb0759290>
compute_id = 'e0dda62779864b00bb6faac8bda6d82d', initial_autoflush = False

    def eval(
        self,
        data_in,
        ds_out: Optional[deeplake.Dataset] = None,
        num_workers: int = 0,
        scheduler: str = "threaded",
        progressbar: bool = True,
        skip_ok: bool = False,
        check_lengths: bool = True,
        pad_data_in: bool = False,
        read_only_ok: bool = False,
        cache_size: int = DEFAULT_TRANSFORM_SAMPLE_CACHE_SIZE,
        checkpoint_interval: int = 0,
        ignore_errors: bool = False,
        verbose: bool = True,
        **kwargs,
    ):
        """Evaluates the pipeline on ``data_in`` to produce an output dataset ``ds_out``.
    
        Args:
            data_in: Input passed to the transform to generate output dataset. Should support \__getitem__ and \__len__. Can be a Deep Lake dataset.
            ds_out (Dataset, optional): - The dataset object to which the transform will get written. If this is not provided, ``data_in`` will be overwritten if it is a Deep Lake dataset, otherwise error will be raised.
                - It should have all keys being generated in output already present as tensors. It's initial state should be either:
                - **Empty**, i.e., all tensors have no samples. In this case all samples are added to the dataset.
                - **All tensors are populated and have same length.** In this case new samples are appended to the dataset.
            num_workers (int): The number of workers to use for performing the transform. Defaults to 0. When set to 0, it will always use serial processing, irrespective of the scheduler.
            scheduler (str): The scheduler to be used to compute the transformation. Supported values include: 'serial', 'threaded', 'processed' and 'ray'.
                Defaults to 'threaded'.
            progressbar (bool): Displays a progress bar if ``True`` (default).
            skip_ok (bool): If ``True``, skips the check for output tensors generated. This allows the user to skip certain tensors in the function definition.
                This is especially useful for inplace transformations in which certain tensors are not modified. Defaults to ``False``.
            check_lengths (bool): If ``True``, checks whether ``ds_out`` has tensors of same lengths initially.
            pad_data_in (bool): If ``True``, pads tensors of ``data_in`` to match the length of the largest tensor in ``data_in``.
                Defaults to ``False``.
            read_only_ok (bool): If ``True`` and output dataset is same as input dataset, the read-only check is skipped.
                Defaults to False.
            cache_size (int): Cache size to be used by transform per worker.
            checkpoint_interval (int): If > 0, the transform will be checkpointed with a commit every ``checkpoint_interval`` input samples to avoid restarting full transform due to intermitten failures. If the transform is interrupted, the intermediate data is deleted and the dataset is reset to the last commit.
                If <= 0, no checkpointing is done. Checkpoint interval should be a multiple of num_workers if num_workers > 0. Defaults to 0.
            ignore_errors (bool): If ``True``, input samples that causes transform to fail will be skipped and the errors will be ignored **if possible**.
            verbose (bool): If ``True``, prints additional information about the transform.
            **kwargs: Additional arguments.
    
        Raises:
            InvalidInputDataError: If ``data_in`` passed to transform is invalid. It should support \__getitem__ and \__len__ operations. Using scheduler other than "threaded" with deeplake dataset having base storage as memory as ``data_in`` will also raise this.
            InvalidOutputDatasetError: If all the tensors of ``ds_out`` passed to transform don't have the same length. Using scheduler other than "threaded" with deeplake dataset having base storage as memory as ``ds_out`` will also raise this.
            TensorMismatchError: If one or more of the outputs generated during transform contain different tensors than the ones present in 'ds_out' provided to transform.
            UnsupportedSchedulerError: If the scheduler passed is not recognized. Supported values include: 'serial', 'threaded', 'processed' and 'ray'.
            TransformError: All other exceptions raised if there are problems while running the pipeline.
            ValueError: If ``num_workers`` > 0 and ``checkpoint_interval`` is not a multiple of ``num_workers`` or if ``checkpoint_interval`` > 0 and ds_out is None.
    
    
        # noqa: DAR401
    
        Example::
    
            @deeplake.compute
            def my_fn(sample_in: Any, samples_out, my_arg0, my_arg1=0):
                samples_out.my_tensor.append(my_arg0 * my_arg1)
    
            # This transform can be used using the eval method in one of these 2 ways:-
    
            # Directly evaluating the method
            # here arg0 and arg1 correspond to the 3rd and 4th argument in my_fn
            my_fn(arg0, arg1).eval(data_in, ds_out, scheduler="threaded", num_workers=5)
    
            # As a part of a Transform pipeline containing other functions
            pipeline = deeplake.compose([my_fn(a, b), another_function(x=2)])
            pipeline.eval(data_in, ds_out, scheduler="processed", num_workers=2)
    
        Note:
            ``pad_data_in`` is only applicable if ``data_in`` is a Deep Lake dataset.
    
        """
        num_workers, scheduler = sanitize_workers_scheduler(num_workers, scheduler)
        overwrite = ds_out is None
        deeplake_reporter.feature_report(
            feature_name="eval",
            parameters={"Num_Workers": str(num_workers), "Scheduler": scheduler},
        )
        check_transform_data_in(data_in, scheduler)
    
        data_in, original_data_in, initial_padding_state = prepare_data_in(
            data_in, pad_data_in, overwrite
        )
        target_ds = data_in if overwrite else ds_out
    
        check_transform_ds_out(
            target_ds, scheduler, check_lengths, read_only_ok and overwrite
        )
    
        # if overwrite then we've already flushed and autocheckecked out data_in which is target_ds now
        if not overwrite:
            target_ds.flush()
            auto_checkout(target_ds)
    
        compute_provider = get_compute_provider(scheduler, num_workers)
        compute_id = str(uuid4().hex)
        target_ds._send_compute_progress(compute_id=compute_id, start=True, progress=0)
    
        initial_autoflush = target_ds.storage.autoflush
        target_ds.storage.autoflush = False
    
        if not check_lengths or read_only_ok:
            skip_ok = True
    
        checkpointing_enabled = checkpoint_interval > 0
        total_samples = len_data_in(data_in)
        if checkpointing_enabled:
            check_checkpoint_interval(
                data_in,
                checkpoint_interval,
                num_workers,
                overwrite,
                verbose,
            )
            datas_in = [
                data_in[i : i + checkpoint_interval]
                for i in range(0, len_data_in(data_in), checkpoint_interval)
            ]
    
        else:
            datas_in = [data_in]
    
        samples_processed = 0
        desc = get_pbar_description(self.functions)
        if progressbar:
            pbar = get_progress_bar(len_data_in(data_in), desc)
            pqueue = compute_provider.create_queue()
        else:
            pbar, pqueue = None, None
        try:
            desc = desc.split()[1]
            completed = False
            progress = 0.0
            for data_in in datas_in:
                if checkpointing_enabled:
                    target_ds._commit(
                        f"Auto-commit during deeplake.compute of {desc} after {progress}% progress",
                        None,
                        False,
                        is_checkpoint=True,
                        total_samples_processed=samples_processed,
                    )
                progress = round(
                    (samples_processed + len_data_in(data_in)) / total_samples * 100, 2
                )
                end = progress == 100
                progress_args = {
                    "compute_id": compute_id,
                    "progress": progress,
                    "end": end,
                }
    
                try:
                    self.run(
                        data_in,
                        target_ds,
                        compute_provider,
                        num_workers,
                        scheduler,
                        progressbar,
                        overwrite,
                        skip_ok,
                        read_only_ok and overwrite,
                        cache_size,
                        pbar,
                        pqueue,
                        ignore_errors,
                        **kwargs,
                    )
                    target_ds._send_compute_progress(**progress_args, status="success")
                    samples_processed += len_data_in(data_in)
                    completed = end
                except Exception as e:
                    if checkpointing_enabled:
                        print(
                            "Transform failed. Resetting back to last committed checkpoint."
                        )
                        target_ds.reset(force=True)
                    target_ds._send_compute_progress(**progress_args, status="failed")
                    index, sample, suggest = None, None, False
                    if isinstance(e, TransformError):
                        index, sample, suggest = e.index, e.sample, e.suggest
                        if checkpointing_enabled and isinstance(index, int):
                            index = samples_processed + index
                        e = e.__cause__  # type: ignore
                    if isinstance(e, AllSamplesSkippedError):
                        raise e
>                   raise TransformError(
                        index=index,
                        sample=sample,
                        samples_processed=samples_processed,
                        suggest=suggest,
                    ) from e
E                   deeplake.util.exceptions.TransformError: Transform failed at index 2 of the input data. See traceback for more details. If you wish to skip the samples that cause errors, please specify `ignore_errors=True`.

deeplake/core/transform/transform.py:322: TransformError

local_path = './hub_pytest/test_deeplake_vectorstore/test_returning_tql_for_exec_option_compute_engine_should_return_correct_tql'
hub_cloud_dev_token = 'eyJhbGciOiJub25lIiwidHlwIjoiSldUIn0.eyJpZCI6InRlc3RpbmdhY2MyIiwiYXBpX2tleSI6IjU4Y0tLb1p6UE1BbThPU2RpbTRiZ2tBekhWekt1VUE3MFJpNTNyZUpKRTJuaiJ9.'

    def test_returning_tql_for_exec_option_compute_engine_should_return_correct_tql(
        local_path,
        hub_cloud_dev_token,
    ):
        db = VectorStore(
            path=local_path,
            token=hub_cloud_dev_token,
        )
    
        texts, embeddings, ids, metadatas, _ = utils.create_data(
            number_of_data=10, embedding_dim=3
        )
    
        db.add(text=texts, embedding=embeddings, id=ids, metadata=metadatas)
    
        query_embedding = np.zeros(3, dtype=np.float32)
>       output = db.search(embedding=query_embedding, return_tql=True)

deeplake\core\vectorstore\test_deeplake_vectorstore.py:2921: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
deeplake\core\vectorstore\deeplake_vectorstore.py:316: in search
    return self.dataset_handler.search(
deeplake\core\vectorstore\deep_memory\deep_memory.py:59: in wrapper
    return func(self, *args, **kwargs)
deeplake\core\vectorstore\dataset_handlers\client_side_dataset_handler.py:235: in search
    return vector_search.search(
deeplake\core\vectorstore\vector_search\vector_search.py:55: in search
    return EXEC_OPTION_TO_SEARCH_TYPE[exec_option](
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

query = None, query_emb = array([0., 0., 0.], dtype=float32)
exec_option = 'python'
dataset = Dataset(path='./hub_pytest/test_deeplake_vectorstore/test_returning_tql_for_exec_option_compute_engine_should_return_correct_tql', tensors=['text', 'metadata', 'embedding', 'id'])
logger = <Logger deeplake.core.vectorstore.deeplake_vectorstore (INFO)>
filter = None, embedding_tensor = 'embedding', distance_metric = 'COS', k = 4
return_tensors = ['text', 'metadata', 'id'], return_view = False
token = 'eyJhbGciOiJub25lIiwidHlwIjoiSldUIn0.eyJpZCI6InRlc3RpbmdhY2MyIiwiYXBpX2tleSI6IjU4Y0tLb1p6UE1BbThPU2RpbTRiZ2tBekhWekt1VUE3MFJpNTNyZUpKRTJuaiJ9.'
org_id = None, return_tql = True

    def vector_search(
        query,
        query_emb,
        exec_option,
        dataset,
        logger,
        filter,
        embedding_tensor,
        distance_metric,
        k,
        return_tensors,
        return_view,
        token,
        org_id,
        return_tql,
    ) -> Union[Dict, DeepLakeDataset]:
        if query is not None:
            raise NotImplementedError(
                f"User-specified TQL queries are not supported for exec_option={exec_option} "
            )
    
        if return_tql:
>           raise NotImplementedError(
                f"return_tql is not supported for exec_option={exec_option}"
            )
E           NotImplementedError: return_tql is not supported for exec_option=python

deeplake\core\vectorstore\vector_search\python\vector_search.py:31: NotImplementedError

distance_metric = 'COS'
hub_cloud_dev_token = 'eyJhbGciOiJub25lIiwidHlwIjoiSldUIn0.eyJpZCI6InRlc3RpbmdhY2MyIiwiYXBpX2tleSI6IjU4Y0tLb1p6UE1BbThPU2RpbTRiZ2tBekhWekt1VUE3MFJpNTNyZUpKRTJuaiJ9.'

    @pytest.mark.slow
    @requires_libdeeplake
    @pytest.mark.parametrize("distance_metric", ["L1", "L2", "COS", "MAX"])
    def test_search_quantitative(distance_metric, hub_cloud_dev_token):
        """Test whether TQL and Python return the same results"""
        # initialize vector store object:
        vector_store = DeepLakeVectorStore(
            path="hub://testingacc2/vectorstore_test",
            read_only=True,
            token=hub_cloud_dev_token,
        )
    
        # use python implementation to search the data
        data_p = vector_store.search(
            embedding=query_embedding, exec_option="python", distance_metric=distance_metric
        )
    
        # use indra implementation to search the data
>       data_ce = vector_store.search(
            embedding=query_embedding,
            exec_option="compute_engine",
            distance_metric=distance_metric,
        )

deeplake/core/vectorstore/test_deeplake_vectorstore.py:649: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
deeplake/core/vectorstore/deeplake_vectorstore.py:316: in search
    return self.dataset_handler.search(
deeplake/core/vectorstore/deep_memory/deep_memory.py:59: in wrapper
    return func(self, *args, **kwargs)
deeplake/core/vectorstore/dataset_handlers/client_side_dataset_handler.py:235: in search
    return vector_search.search(
deeplake/core/vectorstore/vector_search/vector_search.py:55: in search
    return EXEC_OPTION_TO_SEARCH_TYPE[exec_option](
deeplake/core/vectorstore/vector_search/indra/vector_search.py:47: in vector_search
    return vectorstore.indra_search_algorithm(
deeplake/core/vectorstore/vector_search/indra/search_algorithm.py:213: in search
    return searcher.run(
deeplake/core/vectorstore/vector_search/indra/search_algorithm.py:55: in run
    view = self._get_view(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <deeplake.core.vectorstore.vector_search.indra.search_algorithm.SearchIndra object at 0x7f668f7137c0>
tql_query = 'select id, metadata, text, score from (select *, COSINE_SIMILARITY(embedding, ARRAY[-7.555420398712158, 6.94657135009...0723, 0.6642265319824219, -2.519517421722412, 2.7565696239471436, 9.767061233520508, 4.860687732696533]) DESC limit 4)'
runtime = None

    def _get_view(self, tql_query, runtime: Optional[Dict] = None):
        indra_dataset = self._get_indra_dataset()
>       indra_view = indra_dataset.query(tql_query)
E       indra.api.api.not_enterprise_organization_error: #x1B[31mHigh-Performance Features are not available for this dataset based on the usage plan of its parent organization.
E           Please upgrade the organization to a plan that offers this feature.#x1B[0m

deeplake/core/vectorstore/vector_search/indra/search_algorithm.py:107: not_enterprise_organization_error

local_path = './hub_pytest/test_deeplake_vectorstore/test_returning_tql_for_exec_option_compute_engine_should_return_correct_tql'
hub_cloud_dev_token = 'eyJhbGciOiJub25lIiwidHlwIjoiSldUIn0.eyJpZCI6InRlc3RpbmdhY2MyIiwiYXBpX2tleSI6IjU4Y0tLb1p6UE1BbThPU2RpbTRiZ2tBekhWekt1VUE3MFJpNTNyZUpKRTJuaiJ9.'

    def test_returning_tql_for_exec_option_compute_engine_should_return_correct_tql(
        local_path,
        hub_cloud_dev_token,
    ):
        db = VectorStore(
            path=local_path,
            token=hub_cloud_dev_token,
        )
    
        texts, embeddings, ids, metadatas, _ = utils.create_data(
            number_of_data=10, embedding_dim=3
        )
    
        db.add(text=texts, embedding=embeddings, id=ids, metadata=metadatas)
    
        query_embedding = np.zeros(3, dtype=np.float32)
>       output = db.search(embedding=query_embedding, return_tql=True)

deeplake\core\vectorstore\test_deeplake_vectorstore.py:2921: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
deeplake\core\vectorstore\deeplake_vectorstore.py:316: in search
    return self.dataset_handler.search(
deeplake\core\vectorstore\deep_memory\deep_memory.py:59: in wrapper
    return func(self, *args, **kwargs)
deeplake\core\vectorstore\dataset_handlers\client_side_dataset_handler.py:235: in search
    return vector_search.search(
deeplake\core\vectorstore\vector_search\vector_search.py:55: in search
    return EXEC_OPTION_TO_SEARCH_TYPE[exec_option](
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

query = None, query_emb = array([0., 0., 0.], dtype=float32)
exec_option = 'python'
dataset = Dataset(path='./hub_pytest/test_deeplake_vectorstore/test_returning_tql_for_exec_option_compute_engine_should_return_correct_tql', tensors=['text', 'metadata', 'embedding', 'id'])
logger = <Logger deeplake.core.vectorstore.deeplake_vectorstore (INFO)>
filter = None, embedding_tensor = 'embedding', distance_metric = 'COS', k = 4
return_tensors = ['text', 'metadata', 'id'], return_view = False
token = 'eyJhbGciOiJub25lIiwidHlwIjoiSldUIn0.eyJpZCI6InRlc3RpbmdhY2MyIiwiYXBpX2tleSI6IjU4Y0tLb1p6UE1BbThPU2RpbTRiZ2tBekhWekt1VUE3MFJpNTNyZUpKRTJuaiJ9.'
org_id = None, return_tql = True

    def vector_search(
        query,
        query_emb,
        exec_option,
        dataset,
        logger,
        filter,
        embedding_tensor,
        distance_metric,
        k,
        return_tensors,
        return_view,
        token,
        org_id,
        return_tql,
    ) -> Union[Dict, DeepLakeDataset]:
        if query is not None:
            raise NotImplementedError(
                f"User-specified TQL queries are not supported for exec_option={exec_option} "
            )
    
        if return_tql:
>           raise NotImplementedError(
                f"return_tql is not supported for exec_option={exec_option}"
            )
E           NotImplementedError: return_tql is not supported for exec_option=python

deeplake\core\vectorstore\vector_search\python\vector_search.py:31: NotImplementedError