class_docstr: Computes the npairs loss between `y_true` and `y_pred`.
  
  Npairs loss expects paired data where a pair is composed of samples from
  the same labels and each pairs in the minibatch have different labels.
  The loss takes each row of the pair-wise similarity matrix, `y_pred`,
  as logits and the remapped multi-class labels, `y_true`, as labels.
  
  The similarity matrix `y_pred` between two embedding matrices `a` and `b`
  with shape `[batch_size, hidden_size]` can be computed as follows:
  
  >>> a = tf.constant([[1, 2],
  ...                 [3, 4],
  ...                 [5, 6]], dtype=tf.float16)
  >>> b = tf.constant([[5, 9],
  ...                 [3, 6],
  ...                 [1, 8]], dtype=tf.float16)
  >>> y_pred = tf.matmul(a, b, transpose_a=False, transpose_b=True)
  >>> y_pred
  <tf.Tensor: shape=(3, 3), dtype=float16, numpy=
  array([[23., 15., 17.],
     [51., 33., 35.],
     [79., 51., 53.]], dtype=float16)>
  
  <... Note: constants a & b have been used purely for
  example purposes and have no significant value ...>
  
  See: http://www.nec-labs.com/uploads/images/Department-Images/MediaAnalytics/papers/nips16_npairmetriclearning.pdf
  
  Args:
    name: (Optional) name for the loss.
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code: def call(self, y_true, y_pred):
      return npairs_loss(y_true, y_pred)
  
file_path: addons/tensorflow_addons/losses/npairs.py
imports: import tensorflow
  from typeguard import typechecked
  from tensorflow_addons.utils.types import TensorLike
label_desc: Use image node_id 2 for calling the NpairsLoss obj's underlying member method code with example usage: obj.call(y_true, y_pred) and returns: npairs_loss