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
class_docstr_tok: ["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"]
docstr: Computes the offset for universal quantization.
docstr_tok: ["Computes","the","offset","for","universal","quantization","."]
code: def _offset_from_indexes(self, indexes_with_offsets):
      """Computes the offset for universal quantization."""
      offset_indexes = indexes_with_offsets[..., 0]
      return _offset_indexes_to_offset(
          offset_indexes,
          self._num_noise_levels,
          dtype=self.bottleneck_dtype,
      )
  
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 10 for calling the UniversalIndexedEntropyModel obj's underlying member method code with example usage: obj._offset_from_indexes(indexes_with_offsets) and returns: _offset_indexes_to_offset