class_docstr: Indexed entropy model model which implements Universal Quantization.
  
  In contrast to the base class, which uses rounding for quantization, here
  "quantization" is performed additive uniform noise, which is implemented with
  Universal Quantization.
  
  This is described in Sec. 3.2. in the paper
  > "Universally Quantized Neural Compression"<br />
  > Eirikur Agustsson & Lucas Theis<br />
  > https://arxiv.org/abs/2006.09952
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docstr: Decompresses a tensor.
  
  Reconstructs the quantized tensor from bit strings produced by `compress()`.
  
  Args:
    strings: `tf.Tensor` containing the compressed bit strings.
    indexes: `tf.Tensor` specifying the scalar distribution for each output
      element. See class docstring for examples.
  
  Returns:
    A `tf.Tensor` of the same shape as `indexes` (without the optional channel
    dimension).
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code: def decompress(self, strings, indexes):
      """Decompresses a tensor.
  
      Reconstructs the quantized tensor from bit strings produced by `compress()`.
  
      Args:
        strings: `tf.Tensor` containing the compressed bit strings.
        indexes: `tf.Tensor` specifying the scalar distribution for each output
          element. See class docstring for examples.
  
      Returns:
        A `tf.Tensor` of the same shape as `indexes` (without the optional channel
        dimension).
      """
      indexes = _add_offset_indexes(indexes, self._num_noise_levels)
      indexes = self._normalize_indexes(indexes)
      flat_indexes = self._flatten_indexes(indexes)
      symbols_shape = tf.shape(flat_indexes)
      decode_shape = symbols_shape[-self.coding_rank :]
      handle = gen_ops.create_range_decoder(strings, self.cdf)
      handle, symbols = gen_ops.entropy_decode_index(
          handle, flat_indexes, decode_shape, self.cdf_offset.dtype
      )
      sanity = gen_ops.entropy_decode_finalize(handle)
      if self.decode_sanity_check:
          with tf.control_dependencies(
              [
                  tf.debugging.assert_equal(
                      sanity, True, message="Sanity check failed."
                  )
              ]
          ):
              symbols = tf.identity(symbols)
      symbols += tf.gather(self.cdf_offset, flat_indexes)
      offset = self._offset_from_indexes(indexes)
      return tf.cast(symbols, self.bottleneck_dtype) + offset
  
imports: import functools
  import tensorflow
  from tensorflow_compression.python.entropy_models import continuous_base
  from tensorflow_compression.python.ops import gen_ops
  from tensorflow_compression.python.ops import math_ops
label_desc: Use image node_id 13 for calling the UniversalIndexedEntropyModel obj's underlying member method code with example usage: obj.decompress(strings, indexes) and returns: unknown