class_docstr: Classifier based on cosine similarity between vectorized sentences
  
  Parameters:
      save_path: path to save the model
      load_path: path to load the model
class_docstr_tok: ["Classifier","based","on","cosine","similarity","between","vectorized","sentences","Parameters",":","save_path",":","path","to","save","the","model","load_path",":","path","to","load","the","model"]
code: def __init__(
      self,
      top_n: int = 1,
      save_path: str = None,
      load_path: str = None,
      **kwargs,
  ) -> None:
      super().__init__(
          save_path=save_path, load_path=load_path, **kwargs
      )
      self.top_n = top_n
  
      self.x_train_features = self.y_train = None
  
      if kwargs["mode"] != "train":
          self.load()
  
file_path: DeepPavlov/deeppavlov/models/classifiers/cos_sim_classifier.py
imports: from logging import getLogger
  from typing import List, Tuple, Union
  import numpy
  from scipy.sparse import vstack, csr_matrix
  from scipy.sparse.linalg import norm
  from deeppavlov.core.common.file import load_pickle
  from deeppavlov.core.common.file import save_pickle
  from deeppavlov.core.common.registry import register
  from deeppavlov.core.models.estimator import Estimator
  from deeppavlov.core.models.serializable import Serializable
label_desc: Use image node_id 1 to create a new CosineSimilarityClassifier object from inherited base classes: Estimator, Serializable with example: obj = CosineSimilarityClassifier(top_n, save_path, load_path)