class_docstr: Defines a common interface for all recommendation models
class_docstr_tok: ["Defines","a","common","interface","for","all","recommendation","models"]
docstr: Loads the model from a file
  
  Parameters
  ----------
  fileobj_or_path : str or io.IOBase
      Either the filename or an open file-like object to load the model from
  
  Returns
  -------
  RecommenderBase
      The model loaded up from disk
  
  See Also
  --------
  save
  numpy.load
docstr_tok: ["Loads","the","model","from","a","file","Parameters","--","--","--","--","--","fileobj_or_path",":","str","or","io.IOBase","Either","the","filename","or","an","open","file-like","object","to","load","the","model","from","Returns","--","--","--","-","RecommenderBase","The","model","loaded","up","from","disk","See","Also","--","--","--","--","save","numpy.load"]
code: def load(cls, fileobj_or_path) -> "RecommenderBase":
      """Loads the model from a file
  
      Parameters
      ----------
      fileobj_or_path : str or io.IOBase
          Either the filename or an open file-like object to load the model from
  
      Returns
      -------
      RecommenderBase
          The model loaded up from disk
  
      See Also
      --------
      save
      numpy.load
      """
      if isinstance(
          fileobj_or_path, str
      ) and not fileobj_or_path.endswith(".npz"):
          fileobj_or_path = fileobj_or_path + ".npz"
      with np.load(fileobj_or_path, allow_pickle=False) as data:
          ret = cls()
          for k, v in data.items():
              if k == "dtype":
                  v = np.dtype(str(v))
              elif v.shape == ():
                  v = v.item()
              setattr(ret, k, v)
          return ret
  
file_path: implicit/implicit/recommender_base.py
imports: import warnings
  from abc import ABCMeta, abstractmethod
  import numpy
label_desc: Use image node_id 6 for calling the RecommenderBase obj's underlying member method code with example usage: obj.load(cls, fileobj_or_path) and returns: ret