class_docstr: Encoding stage performing uniform quantization using PRNG.
  
  Different from `UniformQuantizationEncodingStage`, which uses deterministic
  decoding, this stage uses a single integer seed as the same source of
  randomness in encode and decode methods, in order to, in the decode method,
  differently estimate the original values before encoding was applied.
  
  In particular, given a floating point input `x` to the `encode` method, the
  output will have the same `dtype` as `x`, with values being "floating point
  integers" in the range `[0, 2**bits-1]`.
  
  The shape of the input `x` to the `encode` method must be statically known.
  
  The extreme points of the quantized interval will correspond to the min and
  max values of the input `x`.
  
  The output of the `decode` method is an unbiased estimation of the original
  values before quantization, but note that it is possible for some of the
  decoded values to be outside of the original range.
docstr: See base class.
code: def get_params(self):
      """See base class."""
      params = collections.OrderedDict(
          [(self.MAX_INT_VALUE_PARAMS_KEY, 2**self._bits - 1)]
      )
      return params, params
  
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
  from tensorflow_model_optimization.python.core.internal.tensor_encoding.utils import tf_utils
label_desc: Use image node_id 6 for calling the PRNGUniformQuantizationEncodingStage obj's underlying member method code with example usage: obj.get_params() and returns: params,  params