code: def to_client(self) -> ClientTokenClassificationRecord:
      return ClientTokenClassificationRecord(
          text=self.text,
          tokens=self.tokens,
          prediction=[
              (ent.label, ent.start, ent.end, ent.score)
              for ent in self.prediction.entities
          ]
          if self.prediction
          else None,
          prediction_agent=self.prediction.agent
          if self.prediction
          else None,
          annotation=[
              (ent.label, ent.start, ent.end)
              for ent in self.annotation.entities
          ]
          if self.annotation
          else None,
          annotation_agent=self.annotation.agent
          if self.annotation
          else None,
          vectors=self._to_client_vectors(self.vectors),
          id=self.id,
          event_timestamp=self.event_timestamp,
          status=self.status,
          metadata=self.metadata or {},
          metrics=self.metrics or None,
          search_keywords=self.search_keywords or None,
      )
  
imports: from datetime import datetime
  from typing import Dict, List, Optional, Union
  from argilla._constants import DEFAULT_MAX_KEYWORD_LENGTH
  from argilla.client.models import TokenClassificationRecord
  from argilla.client.sdk.commons.models import MACHINE_NAME, BaseAnnotation, BaseRecord, PredictionStatus, ScoreRange, SdkVectorSearch, TaskStatus, UpdateDatasetRequest
  from argilla.pydantic_v1 import BaseModel, Field, validator
label_desc: Use image node_id 1 for calling the TokenClassificationRecord obj's underlying member method code with example usage: obj.to_client() and returns: ClientTokenClassificationRecord