code: def validate_tokenized_text(
      tokenized_text: Optional[Sequence[Sequence[str]]],
  ):
      """Validate tokenized text format."""
      error_string = (
          "tokenized_text must be a Sequence of Sequences of strings"
      )
      if not is_sequence_not_str(tokenized_text):
          raise DeepchecksValueError(error_string)
      if not all(is_sequence_not_str(x) for x in tokenized_text):
          raise DeepchecksValueError(error_string)
      if not all(
          isinstance(x, str)
          for tokens in tokenized_text
          for x in tokens
      ):
          raise DeepchecksValueError(error_string)
  
imports: import collections
  from typing import TYPE_CHECKING, Any, Dict, List, NamedTuple, Optional, Sequence, Set, Tuple, Type, cast
  import numpy
  import pandas
  from deepchecks.core.errors import DeepchecksValueError, ValidationError
  from deepchecks.nlp.task_type import TaskType, TTextLabel
  from deepchecks.utils.logger import get_logger
  from deepchecks.utils.metrics import is_label_none
  from deepchecks.utils.type_inference import infer_categorical_features, infer_numerical_features
  from deepchecks.utils.validation import is_sequence_not_str
label_desc: Use image node_id 1 for calling a global function with example usage: validate_tokenized_text(tokenized_text) without return types