code: def __init__(self, **params: Any) -> None:
      # Attr names
      mode = "mode"
      min_gram_length = "min_gram_length"
      max_gram_length = "max_gram_length"
      max_skip_count = "max_skip_count"
      ngram_counts = "ngram_counts"
      ngram_indexes = "ngram_indexes"
      pool_int64s = "pool_int64s"
  
      required_attr = [
          mode,
          min_gram_length,
          max_gram_length,
          max_skip_count,
          ngram_counts,
          ngram_indexes,
          pool_int64s,
      ]
  
      for i in required_attr:
          assert i in params, f"Missing attribute: {i}"
  
      self.mode = params[mode]
      self.min_gram_length = params[min_gram_length]
      self.max_gram_length = params[max_gram_length]
      self.max_skip_count = params[max_skip_count]
      self.ngram_counts = params[ngram_counts]
      self.ngram_indexes = params[ngram_indexes]
      self.pool_int64s = params[pool_int64s]
  
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 1 to create a new TfIdfVectorizerHelper object  with example: obj = TfIdfVectorizerHelper()