class_docstr: Abstract class for the loss function.
class_docstr_tok: ["Abstract","class","for","the","loss","function","."]
docstr: Initializes the hyperparameters needed by the algorithm.
  
  Parameters
  ----------
  hyperparam_dict : dict
      The Loss will check the keys to get the corresponding parameters.
docstr_tok: ["Initializes","the","hyperparameters","needed","by","the","algorithm",".","Parameters","--","--","--","--","--","hyperparam_dict",":","dict","The","Loss","will","check","the","keys","to","get","the","corresponding","parameters","."]
code: def _init_hyperparams(self, hyperparam_dict):
      """Initializes the hyperparameters needed by the algorithm.
  
      Parameters
      ----------
      hyperparam_dict : dict
          The Loss will check the keys to get the corresponding parameters.
      """
      msg = "This function is a placeholder in an abstract class."
      logger.error(msg)
      raise NotImplementedError(msg)
  
imports: import abc
  import logging
  import six
  import tensorflow
  from tensorflow.python.keras import metrics
  from tensorflow.python.keras.utils import losses_utils
  from tensorflow.python.ops import math_ops
label_desc: Use image node_id 4 for calling the Loss obj's underlying member method code with example usage: obj._init_hyperparams(hyperparam_dict) without return types