class_docstr: Quantize tensor based on min/max of tensor values across all batches.
class_docstr_tok: ["Quantize","tensor","based","on","min\/max","of","tensor","values","across","all","batches","."]
code: def build(self, tensor_shape, name, layer):
      min_weight = layer.add_weight(
          name + "_min",
          initializer=keras.initializers.Constant(0.0),
          trainable=False,
      )
      max_weight = layer.add_weight(
          name + "_max",
          initializer=keras.initializers.Constant(0.0),
          trainable=False,
      )
      return {"min_var": min_weight, "max_var": max_weight}
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import abc
  import six
  import tensorflow
  from tensorflow_model_optimization.python.core.quantization.keras import quant_ops
label_desc: Use image node_id 2 for calling the AllValuesQuantizer obj's underlying member method code with example usage: obj.build(tensor_shape, name, layer) and returns: dict