class_docstr: Abstract class for the loss function.
class_docstr_tok: ["Abstract","class","for","the","loss","function","."]
docstr: Broadcast the ``score_pos`` to be of size equal to the number of corruptions.
  
  Parameters
  ----------
  scores_pos : tf.Tensor
      A tensor of scores assigned to positive statements.
  eta : tf.Tensor
      Number of corruptions.
  
  Returns
  -------
  scores_pos : tf.Tensor
      Broadcasted `score_pos`.
docstr_tok: ["Broadcast","the","``","score_pos","``","to","be","of","size","equal","to","the","number","of","corruptions",".","Parameters","--","--","--","--","--","scores_pos",":","tf.Tensor","A","tensor","of","scores","assigned","to","positive","statements",".","eta",":","tf.Tensor","Number","of","corruptions",".","Returns","--","--","--","-","scores_pos",":","tf.Tensor","Broadcasted","`","score_pos","`","."]
code: def _broadcast_score_pos(self, scores_pos, eta):
      """Broadcast the ``score_pos`` to be of size equal to the number of corruptions.
  
      Parameters
      ----------
      scores_pos : tf.Tensor
          A tensor of scores assigned to positive statements.
      eta : tf.Tensor
          Number of corruptions.
  
      Returns
      -------
      scores_pos : tf.Tensor
          Broadcasted `score_pos`.
      """
      scores_pos = tf.reshape(
          tf.tile(scores_pos, [eta]), [eta, tf.shape(scores_pos)[0]]
      )
      return scores_pos
  
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 6 for calling the Loss obj's underlying member method code with example usage: obj._broadcast_score_pos(scores_pos, eta) and returns: scores_pos