class_docstr: QuantizeInfo.
class_docstr_tok: ["QuantizeInfo","."]
docstr: QuantizeInfo.
  
  Args:
    layer_type: Type of keras layer.
    weight_attrs: List of quantizable weight attributes of layer.
    activation_attrs: List of quantizable activation attributes of layer.
    quantize_output: Bool. Should we quantize the output of the layer.
    num_bits_weight: Int. The number of bits for the weight. Default to 8.
    num_bits_activation: Int. The number of bits for the activation.
                         Default to 8.
docstr_tok: ["QuantizeInfo",".","Args",":","layer_type",":","Type","of","keras","layer",".","weight_attrs",":","List","of","quantizable","weight","attributes","of","layer",".","activation_attrs",":","List","of","quantizable","activation","attributes","of","layer",".","quantize_output",":","Bool",".","Should","we","quantize","the","output","of","the","layer",".","num_bits_weight",":","Int",".","The","number","of","bits","for","the","weight",".","Default","to","8.","num_bits_activation",":","Int",".","The","number","of","bits","for","the","activation",".","Default","to","8","."]
code: def __init__(
      self,
      layer_type,
      weight_attrs,
      activation_attrs,
      quantize_output=False,
      num_bits_weight=8,
      num_bits_activation=8,
  ):
      """QuantizeInfo.
  
      Args:
        layer_type: Type of keras layer.
        weight_attrs: List of quantizable weight attributes of layer.
        activation_attrs: List of quantizable activation attributes of layer.
        quantize_output: Bool. Should we quantize the output of the layer.
        num_bits_weight: Int. The number of bits for the weight. Default to 8.
        num_bits_activation: Int. The number of bits for the activation.
                             Default to 8.
      """
      self.layer_type = layer_type
      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
  
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 _QuantizeInfo object from inherited base classes: object with example: obj = _QuantizeInfo(layer_type, weight_attrs, activation_attrs, quantize_output, num_bits_weight, num_bits_activation)