class_docstr: Class for holding hyperparameters for DeepRec algorithms.
class_docstr_tok: ["Class","for","holding","hyperparameters","for","DeepRec","algorithms","."]
docstr: Create an HParams object from a dictionary of hyperparameter values.
  
  Args:
      hparams_dict (dict): Dictionary with the model hyperparameters.
docstr_tok: ["Create","an","HParams","object","from","a","dictionary","of","hyperparameter","values",".","Args",":","hparams_dict","(","dict",")",":","Dictionary","with","the","model","hyperparameters","."]
code: def __init__(self, hparams_dict):
      """Create an HParams object from a dictionary of hyperparameter values.
  
      Args:
          hparams_dict (dict): Dictionary with the model hyperparameters.
      """
      for val in hparams_dict.values():
          if not (
              isinstance(val, int)
              or isinstance(val, float)
              or isinstance(val, str)
              or isinstance(val, list)
          ):
              raise ValueError(
                  "Hyperparameter value {} should be integer, float, string or list.".format(
                      val
                  )
              )
      self._values = hparams_dict
      for hparam in hparams_dict:
          setattr(self, hparam, hparams_dict[hparam])
  
imports: import os
  from sklearn.metrics import roc_auc_score, log_loss, mean_squared_error, accuracy_score, f1_score
  import numpy
  import yaml
  import zipfile
  import pickle
  from recommenders.datasets.download_utils import maybe_download
label_desc: Use image node_id 1 to create a new HParams object  with example: obj = HParams(hparams_dict)