code: def export_tf_onlybigrams_skip5() -> None:
      input = np.array([1, 1, 3, 3, 3, 7, 8, 6, 7, 5, 6, 8]).astype(
          np.int32
      )
      output = np.array([0.0, 0.0, 0.0, 0.0, 1.0, 3.0, 1.0]).astype(
          np.float32
      )
  
      ngram_counts = np.array([0, 4]).astype(np.int64)
      ngram_indexes = np.array([0, 1, 2, 3, 4, 5, 6]).astype(np.int64)
      pool_int64s = np.array(
          [2, 3, 5, 4, 5, 6, 7, 8, 6, 7]
      ).astype(  # unigrams
          np.int64
      )  # bigrams
  
      helper = TfIdfVectorizerHelper(
          mode="TF",
          min_gram_length=2,
          max_gram_length=2,
          max_skip_count=5,
          ngram_counts=ngram_counts,
          ngram_indexes=ngram_indexes,
          pool_int64s=pool_int64s,
      )
      node = helper.make_node_noweights()
      expect(
          node,
          inputs=[input],
          outputs=[output],
          name="test_tfidfvectorizer_tf_onlybigrams_skip5",
      )
  
file_path: onnx/onnx/backend/test/case/node/tfidfvectorizer.py
imports: from typing import Any
  import numpy
  import onnx
  from onnx import NodeProto
  from onnx.backend.test.case.base import Base
  from onnx.backend.test.case.node import expect
label_desc: Use image node_id 4 for calling the TfIdfVectorizer obj's underlying member method code with example usage: obj.export_tf_onlybigrams_skip5() without return types