class_docstr: Quantize tensor based on a moving average of values across batches.
class_docstr_tok: ["Quantize","tensor","based","on","a","moving","average","of","values","across","batches","."]
docstr: Quantize tensor.
  
  Args:
    inputs: Input tensor to be quantized.
    training: Whether the graph is currently training.
    weights: Dictionary of weights the quantizer can use to quantize the
      tensor. This contains the weights created in the `build` function.
    **kwargs: Additional variables which may be passed to the quantizer.
  
  Returns:
    Quantized tensor.
code: def __call__(self, inputs, training, weights, **kwargs):
      """Quantize tensor.
  
      Args:
        inputs: Input tensor to be quantized.
        training: Whether the graph is currently training.
        weights: Dictionary of weights the quantizer can use to quantize the
          tensor. This contains the weights created in the `build` function.
        **kwargs: Additional variables which may be passed to the quantizer.
  
      Returns:
        Quantized tensor.
      """
      return quant_ops.MovingAvgQuantize(
          inputs,
          weights["min_var"],
          weights["max_var"],
          ema_decay=0.999,
          is_training=training,
          num_bits=self.num_bits,
          per_channel=self.per_axis,
          symmetric=self.symmetric,
          narrow_range=self.narrow_range,
      )
  
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 3 for calling the MovingAverageQuantizer obj's underlying member method code with example usage: obj.__call__(inputs, training, weights) and returns: quant_ops