class_docstr: 2x2 Givens rotations.
code: def __init__(self, sizes, args):
      super(RotE, self).__init__(sizes, args)
      self.rel_diag = tf.keras.layers.Embedding(
          input_dim=sizes[1],
          output_dim=self.rank,
          embeddings_initializer=self.initializer,
          embeddings_regularizer=self.rel_regularizer,
          name="rotation_weights",
      )
      self.sim = "dist"
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from kg_hyp_emb.models.base import KGModel
  from kg_hyp_emb.utils import euclidean
  import numpy
  import tensorflow
label_desc: Use image node_id 1 to create a new RotE object from inherited base classes: BaseE with example: obj = RotE(sizes, args)