code: def tokenclassification_records(
      request,
  ) -> List[TokenClassificationRecord]:
      return [
          TokenClassificationRecord(
              text="This is an example",
              tokens=["This", "is", "an", "example"],
              prediction=[("a", 5, 7), ("b", 11, 18)],
              prediction_agent="mock_pagent",
              annotation=[("a", 5, 7)],
              annotation_agent="mock_aagent",
              id=1,
              event_timestamp=datetime.datetime(2000, 1, 1),
              metadata={"mock_metadata": "mock"},
              status="Validated",
          ),
          TokenClassificationRecord(
              text="This is a second example",
              tokens=["This", "is", "a", "second", "example"],
              prediction=[("a", 5, 7), ("b", 8, 9)],
              prediction_agent="mock_pagent",
              id=2,
              event_timestamp=datetime.datetime(2000, 1, 1),
              metadata={"mock_metadata": "mock"},
          ),
          TokenClassificationRecord(
              text="This is a secondd example",
              tokens=["This", "is", "a", "secondd", "example"],
              prediction=[("a", 5, 7), ("b", 8, 9, 0.5)],
              prediction_agent="mock_pagent",
              id=3,
              status="Default",
          ),
          TokenClassificationRecord(
              text="This is a third example",
              tokens=["This", "is", "a", "third", "example"],
              annotation=[("a", 0, 4), ("b", 16, 23)],
              annotation_agent="mock_pagent",
              id="a",
              event_timestamp=datetime.datetime(2000, 1, 1),
              metadata={"mock_metadata": "mock"},
              metrics={},
          ),
          TokenClassificationRecord(
              text="This is a third example",
              tokens=["This", "is", "a", "third", "example"],
              id="b",
              status="Discarded",
              metrics={"mock_metric": ["B", "I", "O"]},
          ),
      ]
  
imports: import datetime
  import random
  from typing import TYPE_CHECKING, Generator, List
  import pytest
  from argilla.client.api import log
  from argilla.client.datasets import read_datasets
  from argilla.client.feedback.dataset.local.dataset import FeedbackDataset
  from argilla.client.feedback.schemas import FeedbackRecord, LabelQuestion, MultiLabelQuestion, RankingQuestion, RatingQuestion, TextField, TextQuestion
  from argilla.client.feedback.schemas.metadata import FloatMetadataProperty, IntegerMetadataProperty, TermsMetadataProperty
  from argilla.client.feedback.schemas.vector_settings import VectorSettings
  from argilla.client.models import Text2TextRecord, TextClassificationRecord, TokenAttributions, TokenClassificationRecord
  from argilla.client.sdk.datasets.models import TaskType
  from argilla.client.sdk.text2text.models import CreationText2TextRecord, Text2TextBulkData
  from argilla.client.sdk.text_classification.models import CreationTextClassificationRecord, TextClassificationBulkData
  from argilla.client.sdk.token_classification.models import CreationTokenClassificationRecord, TokenClassificationBulkData
  from argilla.client.singleton import init
  from argilla.server.models import User
  from datasets import Dataset
  from tests.integration.utils import delete_ignoring_errors
label_desc: Use image node_id 6 for calling a global function with example usage: tokenclassification_records(request) and returns: list