class_docstr: This is an example input baseline generator for DocNN model which uses
  word and dict features.
class_docstr_tok: ["This","is","an","example","input","baseline","generator","for","DocNN","model","which","uses","word","and","dict","features","."]
code: def __init__(self, model, data_handler, device) -> None:
      self.model = model
      self.data_handler = data_handler
      if "dict_feat" in data_handler.features:
          self.vocab_dict = data_handler.features["dict_feat"].vocab
      if "word_feat" in data_handler.features:
          self.vocab_word = data_handler.features["word_feat"].vocab
  
      self.baseline_single_word_feature = (
          self._generate_baseline_single_word_feature(device)
      )
      self.baseline_single_dict_feature = (
          self._generate_baseline_single_dict_feature(device)
      )
  
imports: from collections import defaultdict
  import torch
  from pytext.models.embeddings.dict_embedding import DictEmbedding
  from pytext.models.embeddings.word_embedding import WordEmbedding
  from pytext.models.model import EmbeddingBase, EmbeddingList
label_desc: Use image node_id 1 to create a new BaselineGenerator object  with example: obj = BaselineGenerator(model, data_handler, device)