class_docstr: Provides common code for text vectorizers (tokenization logic).
class_docstr_tok: ["Provides","common","code","for","text","vectorizers","(","tokenization","logic",")","."]
docstr: Creates a tokenized DataFrame from a string Series.
  Each row describes the token string and the corresponding document id.
docstr_tok: ["Creates","a","tokenized","DataFrame","from","a","string","Series",".","Each","row","describes","the","token","string","and","the","corresponding","document","id","."]
code: def _create_tokenized_df(self, docs):
      """
      Creates a tokenized DataFrame from a string Series.
      Each row describes the token string and the corresponding document id.
      """
      min_n, max_n = self.ngram_range
  
      doc_id = cp.arange(start=0, stop=len(docs), dtype=cp.int32)
      doc_id = Series(doc_id)
  
      tokenized_df_ls = [
          self.get_ngrams(docs, n, doc_id)
          for n in range(min_n, max_n + 1)
      ]
      del docs
      tokenized_df = cudf.concat(tokenized_df_ls)
      tokenized_df = tokenized_df.reset_index(drop=True)
  
      return tokenized_df
  
imports: from cuml.internals.safe_imports import cpu_only_import
  import cuml.internals.logger
  from cudf.utils.dtypes import min_signed_type
  from cuml.internals.type_utils import CUPY_SPARSE_DTYPES
  import numbers
  from cuml.internals.safe_imports import gpu_only_import
  from functools import partial
  from cuml.common.sparsefuncs import create_csr_matrix_from_count_df
  from cuml.common.sparsefuncs import csr_row_normalize_l1, csr_row_normalize_l2
  from cuml.feature_extraction._stop_words import ENGLISH_STOP_WORDS
  from cuml.common.exceptions import NotFittedError
  from cuml.internals.safe_imports import gpu_only_import_from
label_desc: Use image node_id 6 for calling the _VectorizerMixin obj's underlying member method code with example usage: obj._create_tokenized_df(docs) and returns: tokenized_df