code: def feedback_dataset_records_with_paired_suggestions() -> (
      List[FeedbackRecord]
  ):
      # This fixture contains the same records as `feedback_dataset_records` but with suggestions
      # for each question so that we can test the annotator metrics.
      # Generates 4 records from 3 annotators.
  
      import random
      import uuid
  
      q1_options = ["positive", "negative"]
      q2_options = [1, 2]
      q3_options = ["a", "b", "c"]
      q4_options = [["a", "b"], ["b", "c"], ["a", "c"]]
      q5_options = [
          [{"rank": 1, "value": "a"}, {"rank": 2, "value": "b"}],
          [{"rank": 2, "value": "a"}, {"rank": 1, "value": "b"}],
          [{"rank": 1, "value": "a"}, {"rank": 2, "value": "b"}],
      ]
  
      records = []
  
      for record_id in range(1, 5):
          responses = []
          for annotator_id in range(1, 4):
              # Make the random seed depend on the record_id and annotator_id for reproducibility.
              random.seed(123 + record_id + annotator_id)
              idx1 = random.randint(0, len(q1_options) - 1)
              random.seed(123 + record_id + annotator_id + 1)
              idx2 = random.randint(0, len(q2_options) - 1)
              random.seed(123 + record_id + annotator_id + 2)
              idx3 = random.randint(0, len(q3_options) - 1)
              random.seed(123 + record_id + annotator_id + 3)
              idx4 = random.randint(0, len(q4_options) - 1)
              random.seed(123 + record_id + annotator_id + 4)
              idx5 = random.randint(0, len(q5_options) - 1)
  
              response_q1 = q1_options[idx1]
              response_q2 = q2_options[idx2]
              response_q3 = q3_options[idx3]
              response_q4 = q4_options[idx4]
              response_q5 = q5_options[idx5]
  
              if annotator_id == 1:
                  # Always answer like the suggestion
                  suggestion_q1 = response_q1
                  suggestion_q2 = response_q2
                  suggestion_q3 = response_q3
                  suggestion_q4 = response_q4
                  suggestion_q5 = response_q5
              elif annotator_id == 2:
                  # Never answer like the suggestion
                  suggestion_q1 = q1_options[idx1 - 1]
                  suggestion_q2 = q2_options[idx2 - 1]
                  suggestion_q3 = q3_options[idx3 - 1]
                  suggestion_q4 = q4_options[idx4 - 1]
                  suggestion_q5 = q5_options[idx5 - 1]
              elif annotator_id == 3:
                  # Sometimes answer like the suggestion
                  if record_id % 2 == 0:
                      suggestion_q1 = response_q1
                      suggestion_q2 = response_q2
                      suggestion_q3 = response_q3
                      suggestion_q4 = response_q4
                      suggestion_q5 = response_q5
                  else:
                      suggestion_q1 = q1_options[idx1 - 1]
                      suggestion_q2 = q2_options[idx2 - 1]
                      suggestion_q3 = q3_options[idx3 - 1]
                      suggestion_q4 = q4_options[idx4 - 1]
                      suggestion_q5 = q5_options[idx5 - 1]
  
              responses.append(
                  {
                      "values": {
                          "question-1": {
                              "value": f"{response_q1} example"
                          },
                          "question-2": {"value": response_q2},
                          "question-3": {"value": response_q3},
                          "question-4": {"value": response_q4},
                          "question-5": {"value": response_q5},
                      },
                      "status": "submitted",
                      "user_id": uuid.UUID(int=annotator_id),
                  },
              )
  
          records.append(
              FeedbackRecord(
                  fields={
                      "text": f"This is a {response_q1} example",
                      "label": f"{response_q1}",
                  },
                  responses=responses,
                  suggestions=[
                      {
                          "question_name": "question-1",
                          "value": suggestion_q1,
                          "type": "human",
                          "score": 0.0,
                          "agent": f"agent-{annotator_id}",
                      },
                      {
                          "question_name": "question-2",
                          "value": suggestion_q2,
                          "type": "human",
                          "score": 0.0,
                          "agent": f"agent-{annotator_id}",
                      },
                      {
                          "question_name": "question-3",
                          "value": suggestion_q3,
                          "type": "human",
                          "score": 0.0,
                          "agent": f"agent-{annotator_id}",
                      },
                      {
                          "question_name": "question-4",
                          "value": suggestion_q4,
                          "type": "human",
                          "score": 0.0,
                          "agent": f"agent-{annotator_id}",
                      },
                      {
                          "question_name": "question-5",
                          "value": suggestion_q5,
                          "type": "human",
                          "score": 0.0,
                          "agent": f"agent-{annotator_id}",
                      },
                  ],
                  metadata={"unit": "test"},
                  external_id=str(annotator_id + record_id),
              )
          )
  
      return records
  
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 17 for calling a global function with example usage: feedback_dataset_records_with_paired_suggestions() and returns: records