code: def load_topic_model(fname):
      """
      Load saved TopicModel object
      Args:
          fname(str): base filename for all saved files
      """
      with open(fname + ".tm_vect", "rb") as f:
          vectorizer = pickle.load(f)
      with open(fname + ".tm_model", "rb") as f:
          model = pickle.load(f)
      with open(fname + ".tm_params", "rb") as f:
          params = pickle.load(f)
      tm = get_topic_model(
          n_topics=params["n_topics"],
          n_features=params["n_features"],
          verbose=params["verbose"],
      )
      tm.model = model
      tm.vectorizer = vectorizer
      return tm
  
file_path: ktrain/ktrain/text/__init__.py
imports: import pickle
  from .None import shallownlp, textutils
  from .data import texts_from_array, texts_from_csv, texts_from_df, texts_from_folder
  from .eda import get_topic_model
  from .models import print_text_classifiers, print_text_regression_models, text_classifier, text_regression_model
  from .ner.data import entities_from_array, entities_from_conll2003, entities_from_df, entities_from_gmb, entities_from_txt
  from .ner.models import print_sequence_taggers, sequence_tagger
  from .preprocessor import Transformer, TransformerEmbedding
  from .qa import AnswerExtractor, SimpleQA
  from .summarization import TransformerSummarizer
  from .textextractor import TextExtractor
  from .textutils import extract_filenames, filter_by_id, load_text_files
  from .translation import EnglishTranslator, Translator
  from .zsl import ZeroShotClassifier
label_desc: Use image node_id 1 for calling a global function with example usage: load_topic_model(fname) and returns: tm