def get_divide_and_conquer_list_of_raw_samples_from_datasource_by_dataset_id(self, dataset_id : Annotated[constr(strict=True), Field(..., description="ObjectId of the dataset")], dnc_shards : Annotated[conint(strict=True, ge=1), Field(..., description="The amount of shards we should split the data into when using the divide and conquer strategy. ")], var_from : Annotated[Optional[conint(strict=True, ge=0)], Field(description="Unix timestamp, only samples with a creation date after `from` will be returned. This parameter is ignored if `cursor` is specified. ")] = None, to : Annotated[Optional[conint(strict=True, ge=0)], Field(description="Unix timestamp, only samples with a creation date before `to` will be returned. This parameter is ignored if `cursor` is specified. ")] = None, relevant_filenames_file_name : Annotated[Optional[constr(strict=True, min_length=4)], Field(description="The name of the file within your datasource which contains a list of relevant filenames to list. See https://docs.lightly.ai/docker/getting_started/first_steps.html#specify-relevant-files for more details  ")] = None, **kwargs) -> DivideAndConquerCursorData:  # noqa: E501
        """divide and conquer the listing of samples files into multiple cursors  # noqa: E501

        Use divide and conquer to get multiple cursors for listing the raw samples  from the datasource for a specific taskName in parallel. The cursors should be used at the endpoint getListOfRawSamplesFromDatasourceByDatasetId. There is no guarantee that the cursors returned are in order, that any cursor will return the same number of items, or that there will be more than one cursor returned.   # noqa: E501
        This method makes a synchronous HTTP request by default. To make an
        asynchronous HTTP request, please pass async_req=True

        >>> thread = api.get_divide_and_conquer_list_of_raw_samples_from_datasource_by_dataset_id(dataset_id, dnc_shards, var_from, to, relevant_filenames_file_name, async_req=True)
        >>> result = thread.get()

        :param dataset_id: ObjectId of the dataset (required)
        :type dataset_id: str
        :param dnc_shards: The amount of shards we should split the data into when using the divide and conquer strategy.  (required)
        :type dnc_shards: int
        :param var_from: Unix timestamp, only samples with a creation date after `from` will be returned. This parameter is ignored if `cursor` is specified. 
        :type var_from: int
        :param to: Unix timestamp, only samples with a creation date before `to` will be returned. This parameter is ignored if `cursor` is specified. 
        :type to: int
        :param relevant_filenames_file_name: The name of the file within your datasource which contains a list of relevant filenames to list. See https://docs.lightly.ai/docker/getting_started/first_steps.html#specify-relevant-files for more details  
        :type relevant_filenames_file_name: str
        :param async_req: Whether to execute the request asynchronously.
        :type async_req: bool, optional
        :param _request_timeout: timeout setting for this request. If one
                                 number provided, it will be total request
                                 timeout. It can also be a pair (tuple) of
                                 (connection, read) timeouts.
        :return: Returns the result object.
                 If the method is called asynchronously,
                 returns the request thread.
        :rtype: DivideAndConquerCursorData
        """
        kwargs['_return_http_data_only'] = True
        if '_preload_content' in kwargs:
            raise ValueError("Error! Please call the get_divide_and_conquer_list_of_raw_samples_from_datasource_by_dataset_id_with_http_info method with `_preload_content` instead and obtain raw data from ApiResponse.raw_data")
        return self.get_divide_and_conquer_list_of_raw_samples_from_datasource_by_dataset_id_with_http_info(dataset_id, dnc_shards, var_from, to, relevant_filenames_file_name, **kwargs)  # noqa: E501
divide and conquer the listing of samples files into multiple cursors  # noqa: E501

        Use divide and conquer to get multiple cursors for listing the raw samples  from the datasource for a specific taskName in parallel. The cursors should be used at the endpoint getListOfRawSamplesFromDatasourceByDatasetId. There is no guarantee that the cursors returned are in order, that any cursor will return the same number of items, or that there will be more than one cursor returned.   # noqa: E501
        This method makes a synchronous HTTP request by default. To make an
        asynchronous HTTP request, please pass async_req=True

        >>> thread = api.get_divide_and_conquer_list_of_raw_samples_from_datasource_by_dataset_id(dataset_id, dnc_shards, var_from, to, relevant_filenames_file_name, async_req=True)
        >>> result = thread.get()

        :param dataset_id: ObjectId of the dataset (required)
        :type dataset_id: str
        :param dnc_shards: The amount of shards we should split the data into when using the divide and conquer strategy.  (required)
        :type dnc_shards: int
        :param var_from: Unix timestamp, only samples with a creation date after `from` will be returned. This parameter is ignored if `cursor` is specified. 
        :type var_from: int
        :param to: Unix timestamp, only samples with a creation date before `to` will be returned. This parameter is ignored if `cursor` is specified. 
        :type to: int
        :param relevant_filenames_file_name: The name of the file within your datasource which contains a list of relevant filenames to list. See https://docs.lightly.ai/docker/getting_started/first_steps.html#specify-relevant-files for more details  
        :type relevant_filenames_file_name: str
        :param async_req: Whether to execute the request asynchronously.
        :type async_req: bool, optional
        :param _request_timeout: timeout setting for this request. If one
                                 number provided, it will be total request
                                 timeout. It can also be a pair (tuple) of
                                 (connection, read) timeouts.
        :return: Returns the result object.
                 If the method is called asynchronously,
                 returns the request thread.
        :rtype: DivideAndConquerCursorData
        
lightly/openapi_generated/swagger_client/api/datasources_api.py