class_docstr: Loads a scikit-learn model and tokenizer (tokenizer implements
  `transform` and model implements `predict_proba`).
  
  May need to be extended and modified for different types of
  tokenizers.
class_docstr_tok: ["Loads","a","scikit-learn","model","and","tokenizer","(","tokenizer","implements","`","transform","`","and","model","implements","`","predict_proba","`",")",".","May","need","to","be","extended","and","modified","for","different","types","of","tokenizers","."]
code: def __call__(self, text_input_list, batch_size=None):
      encoded_text_matrix = self.tokenizer.transform(
          text_input_list
      ).toarray()
      tokenized_text_df = pd.DataFrame(
          encoded_text_matrix,
          columns=self.tokenizer.get_feature_names(),
      )
      return self.model.predict_proba(tokenized_text_df)
  
imports: import pandas
  from .model_wrapper import ModelWrapper
label_desc: Use image node_id 2 for calling the SklearnModelWrapper obj's underlying member method code with example usage: obj.__call__(text_input_list, batch_size) and returns: self