class_docstr: A wrapper class to apply sklearn clustering models on DocumentEmbeddings.
class_docstr_tok: ["A","wrapper","class","to","apply","sklearn","clustering","models","on","DocumentEmbeddings","."]
docstr: Loads a model from a given path.
  
  Args:
      model_file: path to the file where the model is saved.
docstr_tok: ["Loads","a","model","from","a","given","path",".","Args",":","model_file",":","path","to","the","file","where","the","model","is","saved","."]
code: def load(model_file: Union[str, Path]):
      """Loads a model from a given path.
  
      Args:
          model_file: path to the file where the model is saved.
      """
      log.info("Loading model from: " + str(model_file))
      return pickle.loads(joblib.load(str(model_file)))
  
imports: import logging
  import pickle
  from collections import OrderedDict
  from pathlib import Path
  from typing import Optional, Union
  import joblib
  from sklearn.base import BaseEstimator, ClusterMixin
  from sklearn.metrics import normalized_mutual_info_score
  from tqdm import tqdm
  from flair.data import Corpus, _iter_dataset
  from flair.datasets import DataLoader
  from flair.embeddings import DocumentEmbeddings
label_desc: Use image node_id 5 for calling the ClusteringModel obj's underlying member method code with example usage: obj.load(model_file) and returns: pickle