class_docstr: Embedding with the token distributions as a weighted mixture of softmaxes.
  
  Expressing the token distributions in this way improves expressiveness and
  enables the matrix of token probabilities given all contexts to be high rank.
  
  The vocab embedding is the same as the default, which is just a simple
  embedding.
  
  See https://arxiv.org/pdf/1711.03953.pdf for more details.
code: def ids_to_embedding(self, ids: mtf.Tensor, context) -> mtf.Tensor:
      del context
      return mtf.gather(self._embedding_weights, ids, self._vocab_dim)
  
imports: import gin
  import mesh_tensorflow
  from mesh_tensorflow.transformer import transformer
  import tensorflow.compat.v1
label_desc: Use image node_id 2 for calling the MixtureOfSoftmaxes obj's underlying member method code with example usage: obj.ids_to_embedding(ids, context) and returns: mtf