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,
      num_bits_weight: int = 8,
      num_bits_activation: int = 8,
  ):
      self.weight_attrs = weight_attrs
      self.activation_attrs = activation_attrs
      self.quantize_output = quantize_output
      self._num_bits_weight = num_bits_weight
      self._num_bits_activation = num_bits_activation
  
      # 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=num_bits_weight,
          per_axis=False,
          symmetric=True,
          narrow_range=True,
      )  # weight
      self.activation_quantizer = quantizers.MovingAverageQuantizer(
          num_bits=num_bits_activation,
          per_axis=False,
          symmetric=False,
          narrow_range=False,
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from typing import Any, Dict
  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.experimental.default_n_bit import default_n_bit_quantize_configs
  from tensorflow_model_optimization.python.core.quantization.keras.experimental.default_n_bit import default_n_bit_quantizers
label_desc: Use image node_id 1 to create a new DefaultNBitQuantizeConfig object from inherited base classes: QuantizeConfig with example: obj = DefaultNBitQuantizeConfig(weight_attrs, activation_attrs, quantize_output, num_bits_weight, num_bits_activation)