class_docstr: Basic class of models
  
  Attributes:
      hparams (HParams): A HParams object, holds the entire set of hyperparameters.
      train_iterator (object): An iterator to load the data in training steps.
      test_iterator (object): An iterator to load the data in testing steps.
      graph (object): An optional graph.
      seed (int): Random seed.
class_docstr_tok: ["Basic","class","of","models","Attributes",":","hparams","(","HParams",")",":","A","HParams","object",",","holds","the","entire","set","of","hyperparameters",".","train_iterator","(","object",")",":","An","iterator","to","load","the","data","in","training","steps",".","test_iterator","(","object",")",":","An","iterator","to","load","the","data","in","testing","steps",".","graph","(","object",")",":","An","optional","graph",".","seed","(","int",")",":","Random","seed","."]
docstr: Load pre-trained embeddings as a constant tensor.
  
  Args:
      file_path (str): the pre-trained glove embeddings file path.
  
  Returns:
      numpy.ndarray: A constant numpy array.
docstr_tok: ["Load","pre-trained","embeddings","as","a","constant","tensor",".","Args",":","file_path","(","str",")",":","the","pre-trained","glove","embeddings","file","path",".","Returns",":","numpy.ndarray",":","A","constant","numpy","array","."]
code: def _init_embedding(self, file_path):
      """Load pre-trained embeddings as a constant tensor.
  
      Args:
          file_path (str): the pre-trained glove embeddings file path.
  
      Returns:
          numpy.ndarray: A constant numpy array.
      """
  
      return np.load(file_path)
  
imports: import abc
  import time
  import numpy
  from tqdm import tqdm
  import tensorflow
  from tensorflow.compat.v1 import keras
  from recommenders.models.deeprec.deeprec_utils import cal_metric
label_desc: Use image node_id 2 for calling the BaseModel obj's underlying member method code with example usage: obj._init_embedding(file_path) and returns: np