class_docstr: Negative Log-Likelihood loss.
  
  As described in :cite:`trouillon2016complex`.
  
  .. math::
  
      \mathcal{L}(\Theta) = \sum_{t \in \mathcal{G} \cup \mathcal{C}}log(1 + exp(-y \, f_{model}(t;\Theta)))
  
  where :math:`y \in \{-1, 1\}` is the label of the statement, :math:`\mathcal{G}` is the set of positives,
  :math:`\mathcal{C}` is the set of corruptions and :math:`f_{model}(t;\Theta)` is the model-specific scoring function.
  
  Example
  -------
  >>> import ampligraph.latent_features.loss_functions as lfs
  >>> loss = lfs.NLLLoss({'reduction': 'mean'})
  >>> isinstance(loss, lfs.NLLLoss)
  True
  
  >>> loss = lfs.get('nll')
  >>> isinstance(loss, lfs.NLLLoss)
  True
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docstr: Initializes the hyperparameters needed by the algorithm.
  
  Parameters
  ----------
  hyperparam_dict : dictionary
      The Loss will check the keys to get the corresponding parameters.
docstr_tok: ["Initializes","the","hyperparameters","needed","by","the","algorithm",".","Parameters","--","--","--","--","--","hyperparam_dict",":","dictionary","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 : dictionary
          The Loss will check the keys to get the corresponding parameters.
      """
      return
  
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 NLLLoss obj's underlying member method code with example usage: obj._init_hyperparams(hyperparam_dict) without return types