class_docstr: Wraps a loss function in the `Loss` class.
  
  Example
  -------
  >>> import ampligraph.latent_features.loss_functions as lfs
  >>> def user_defined_loss(scores_pos, scores_neg):
  >>>    neg_exp = tf.exp(scores_neg)
  >>>    pos_exp = tf.exp(scores_pos)
  >>>    softmax_score = pos_exp / (tf.reduce_sum(neg_exp, axis=0) + pos_exp)
  >>>    loss = -tf.math.log(softmax_score)
  >>>    return loss
  >>> udf_loss = lfs.get(user_defined_loss)
  >>> isinstance(udf_loss, Loss)
  True
  >>> isinstance(udf_loss, LossFunctionWrapper)
  True
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docstr: Verifies and stores the hyperparameters needed by the algorithm.
  
  Parameters
  ----------
  hyperparam_dict : dict
      The Loss will check the keys to get the corresponding parameters.
docstr_tok: ["Verifies","and","stores","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={}):
      """Verifies and stores the hyperparameters needed by the algorithm.
  
      Parameters
      ----------
      hyperparam_dict : dict
          The Loss will check the keys to get the corresponding parameters.
      """
      pass
  
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 2 for calling the LossFunctionWrapper obj's underlying member method code with example usage: obj._init_hyperparams(hyperparam_dict) without return types