class_docstr: Encoding stage performing uniform quantization.
  
  This class performs quantization to uniformly spaced values, without realizing
  any savings by itself.
  
  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]`.
  
  If `min_max` is not provided, the extreme points of the quantized interval
  will correspond to the min and max values of the input `x`. If `min_max` is
  provided, the input `x` is first clipped to this range, and the extreme points
  of the quantized interval correspond to the provided `min_max` values.
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docstr: See base class.
code: def name(self):
      """See base class."""
      return "uniform_quantization"
  
imports: import collections
  import numpy
  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 2 for calling the UniformQuantizationEncodingStage obj's underlying member method code with example usage: obj.name() and returns: str