class_docstr: Sparse id to dense vector embedding.
code: def __init__(
      self,
      max_id,
      dim,
      initializer=lambda: tf.truncated_normal_initializer(
          stddev=0.0002
      ),
      combiner="sum",
      **kwargs,
  ):
      super(SparseEmbedding, self).__init__(
          max_id=max_id, dim=dim, initializer=initializer, **kwargs
      )
      self.combiner = combiner
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  import tensorflow
  from tensorflow.python.util import nest
label_desc: Use image node_id 1 to create a new SparseEmbedding object from inherited base classes: Embedding with example: obj = SparseEmbedding(max_id, dim, initializer, combiner)