code: def vectorizer_and_dtm(tokenized_docs):
      vectorizer = representations.Vectorizer(
          tf_type="linear",
          norm=None,
          min_df=1,
          max_df=1.0,
          max_n_terms=None,
      )
      doc_term_matrix = vectorizer.fit_transform(tokenized_docs)
      return vectorizer, doc_term_matrix
  
imports: import pytest
  import numpy
  from scipy.sparse import coo_matrix
  from textacy import extract, representations
label_desc: Use image node_id 3 for calling a global function with example usage: vectorizer_and_dtm(tokenized_docs) and returns: vectorizer,  doc_term_matrix