class_docstr: QuantizeConfig for non recurrent Keras layers.
class_docstr_tok: ["QuantizeConfig","for","non","recurrent","Keras","layers","."]
code: def __init__(self, weight_attrs, activation_attrs, quantize_output):
      self.weight_attrs = weight_attrs
      self.activation_attrs = activation_attrs
      self.quantize_output = quantize_output
  
      # TODO(pulkitb): For some layers such as Conv2D, per_axis should be True.
      # Add mapping for which layers support per_axis.
      self.weight_quantizer = quantizers.LastValueQuantizer(
          num_bits=8, per_axis=False, symmetric=True, narrow_range=True
      )
      self.activation_quantizer = quantizers.MovingAverageQuantizer(
          num_bits=8,
          per_axis=False,
          symmetric=False,
          narrow_range=False,
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow
  from tensorflow_model_optimization.python.core.quantization.keras import quantize_config
  from tensorflow_model_optimization.python.core.quantization.keras import quantize_registry
  from tensorflow_model_optimization.python.core.quantization.keras import quantizers
  from tensorflow_model_optimization.python.core.quantization.keras.default_8bit import default_8bit_quantize_configs
  from tensorflow_model_optimization.python.core.quantization.keras.default_8bit import default_8bit_quantizers
label_desc: Use image node_id 1 to create a new Default8BitQuantizeConfig object from inherited base classes: QuantizeConfig with example: obj = Default8BitQuantizeConfig(weight_attrs, activation_attrs, quantize_output)