class_docstr: Encoding stage splitting the input by small values.
  
  This encoding stage will split the input into two outputs: the value and the
  indices of the elements whose absolute value is larger than a certain
  threshold. The elements smaller than the threshold is then decoded to zero.
docstr: See base class.
docstr_tok: ["See","base","class","."]
code: def decode(
      self,
      encoded_tensors,
      decode_params,
      num_summands=None,
      shape=None,
  ):
      """See base class."""
      del decode_params, num_summands  # Unused.
  
      indices = encoded_tensors[self.ENCODED_INDICES_KEY]
      non_zero_x = encoded_tensors[self.ENCODED_VALUES_KEY]
  
      indices = tf.expand_dims(indices, 1)
  
      indices = tf.cast(indices, tf.int64)
      shape = tf.cast(shape, tf.int64)
      sparse_tensor = tf.SparseTensor(
          indices=indices, values=non_zero_x, dense_shape=shape
      )
      decoded_x = tf.sparse.to_dense(sparse_tensor)
  
      return decoded_x
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  import tensorflow
  from tensorflow_model_optimization.python.core.internal.tensor_encoding.core import encoding_stage
label_desc: Use image node_id 8 for calling the SplitBySmallValueEncodingStage obj's underlying member method code with example usage: obj.decode(encoded_tensors, decode_params, num_summands, shape) and returns: decoded_x