class_docstr: The main argilla client.
class_docstr_tok: ["The","main","argilla","client","."]
docstr: Loads a argilla dataset.
  
  Args:
      name: The dataset name.
      query: An ElasticSearch query with the `query string
          syntax <https://docs.argilla.io/en/latesthttps://docs.argilla.io/en/latest/practical_guides/filter_dataset.html.html>`_
      vector: Vector configuration for a semantic search
      ids: If provided, load dataset records with given ids.
      limit: The number of records to retrieve.
      sort: The fields on which to sort [(<field_name>, 'asc|decs')].
      id_from: If provided, starts gathering the records starting from that Record.
          As the Records returned with the load method are sorted by ID, ´id_from´
          can be used to load using batches.
      batch_size: If provided, load `batch_size` samples per request. A lower batch
          size may help avoid timeouts.
      include_vectors: When set to `False`, indicates that the record data will be retrieved excluding its vectors,
          if any. By default, this parameter is set to `True`, meaning that vector data will be included.
      include_metrics: When set to `False`, indicates that the record data will be retrieved excluding its metrics.
          By default, this parameter is set to `True`, meaning that metrics will be included.
      as_pandas: DEPRECATED! To get a pandas DataFrame do
          ``rg.load('my_dataset').to_pandas()``.
  
  
  Returns:
      A argilla dataset.
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code: def load(
      self,
      name: str,
      workspace: Optional[str] = None,
      query: Optional[str] = None,
      vector: Optional[Tuple[str, List[float]]] = None,
      ids: Optional[List[Union[str, int]]] = None,
      limit: Optional[int] = None,
      sort: Optional[List[Tuple[str, str]]] = None,
      id_from: Optional[str] = None,
      batch_size: int = 250,
      include_vectors: bool = True,
      include_metrics: bool = True,
      as_pandas=None,
  ) -> Dataset:
      """Loads a argilla dataset.
  
      Args:
          name: The dataset name.
          query: An ElasticSearch query with the `query string
              syntax <https://docs.argilla.io/en/latesthttps://docs.argilla.io/en/latest/practical_guides/filter_dataset.html.html>`_
          vector: Vector configuration for a semantic search
          ids: If provided, load dataset records with given ids.
          limit: The number of records to retrieve.
          sort: The fields on which to sort [(<field_name>, 'asc|decs')].
          id_from: If provided, starts gathering the records starting from that Record.
              As the Records returned with the load method are sorted by ID, ´id_from´
              can be used to load using batches.
          batch_size: If provided, load `batch_size` samples per request. A lower batch
              size may help avoid timeouts.
          include_vectors: When set to `False`, indicates that the record data will be retrieved excluding its vectors,
              if any. By default, this parameter is set to `True`, meaning that vector data will be included.
          include_metrics: When set to `False`, indicates that the record data will be retrieved excluding its metrics.
              By default, this parameter is set to `True`, meaning that metrics will be included.
          as_pandas: DEPRECATED! To get a pandas DataFrame do
              ``rg.load('my_dataset').to_pandas()``.
  
  
      Returns:
          A argilla dataset.
  
      """
      if workspace is not None:
          self.set_workspace(workspace)
  
      if as_pandas is False:
          warnings.warn(
              "The argument `as_pandas` is deprecated and will be removed in a future"
              " version. Please adapt your code accordingly. ",
              FutureWarning,
          )
      elif as_pandas is True:
          raise ValueError(
              "The argument `as_pandas` is deprecated and will be removed in a future"
              " version. Please adapt your code accordingly. ",
              "If you want a pandas DataFrame do `rg.load('my_dataset').to_pandas()`.",
          )
  
      return self._load_records_internal(
          name=name,
          query=query,
          vector=vector,
          ids=ids,
          limit=limit,
          sort=sort,
          id_from=id_from,
          batch_size=batch_size,
          include_vectors=include_vectors,
          include_metrics=include_metrics,
      )
  
imports: import logging
  import os
  import re
  import warnings
  from asyncio import Future
  from concurrent.futures import ThreadPoolExecutor
  from typing import Any, Dict, Iterable, List, Optional, Tuple, Union
  import backoff
  import httpx
  from rich import print
  from rich.progress import Progress
  from argilla._constants import DATASET_NAME_REGEX_PATTERN, DEFAULT_API_KEY, DEFAULT_API_URL, DEFAULT_USERNAME, WORKSPACE_HEADER_NAME, WORKSPACE_NAME_REGEX_PATTERN
  from argilla.client.apis.datasets import Datasets
  from argilla.client.apis.metrics import MetricsAPI
  from argilla.client.apis.search import Search, VectorSearch
  from argilla.client.apis.status import Status
  from argilla.client.datasets import Dataset, DatasetForText2Text, DatasetForTextClassification, DatasetForTokenClassification
  from argilla.client.metrics.models import MetricResults
  from argilla.client.models import BulkResponse, Record, Text2TextRecord, TextClassificationRecord, TokenClassificationRecord
  from argilla.client.sdk.client import AuthenticatedClient
  from argilla.client.sdk.commons.api import bulk
  from argilla.client.sdk.commons.errors import AlreadyExistsApiError, InputValueError, NotFoundApiError
  from argilla.client.sdk.datasets import api
  from argilla.client.sdk.datasets.models import CopyDatasetRequest, TaskType
  from argilla.client.sdk.datasets.models import Dataset
  from argilla.client.sdk.metrics import api
  from argilla.client.sdk.metrics.models import MetricInfo
  from argilla.client.sdk.text2text.models import CreationText2TextRecord, Text2TextBulkData
  from argilla.client.sdk.text2text.models import Text2TextRecord
  from argilla.client.sdk.text_classification import api
  from argilla.client.sdk.text_classification.models import CreationTextClassificationRecord, LabelingRule, LabelingRuleMetricsSummary, TextClassificationBulkData
  from argilla.client.sdk.text_classification.models import TextClassificationRecord
  from argilla.client.sdk.token_classification.models import CreationTokenClassificationRecord, TokenClassificationBulkData
  from argilla.client.sdk.token_classification.models import TokenClassificationRecord
  from argilla.client.sdk.users import api
  from argilla.client.sdk.v1.workspaces import api
  from argilla.client.sdk.v1.workspaces.models import WorkspaceModel
label_desc: Use image node_id 19 for calling the Argilla obj's underlying member method code with example usage: obj.load(name, workspace, query, vector, ids, limit, sort, id_from, batch_size, include_vectors, include_metrics, as_pandas) and returns: self