class_docstr: QuantizeConfig for Conv2DTranspose layers.
class_docstr_tok: ["QuantizeConfig","for","Conv2DTranspose","layers","."]
code: def __init__(
      self,
      weight_attrs,
      activation_attrs,
      quantize_output,
      num_bits_weight: int = 8,
      num_bits_activation: int = 8,
  ):
      super(DefaultNBitConvTransposeQuantizeConfig, self).__init__(
          weight_attrs,
          activation_attrs,
          quantize_output,
          num_bits_weight,
          num_bits_activation,
      )
      self._num_bits_weight = num_bits_weight
      self._num_bits_activation = num_bits_activation
  
      self.weight_quantizer = (
          n_bit_quantizers.DefaultNBitConvTransposeWeightsQuantizer(
              num_bits_weight, 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 DefaultNBitConvTransposeQuantizeConfig object from inherited base classes: DefaultNBitQuantizeConfig with example: obj = DefaultNBitConvTransposeQuantizeConfig(weight_attrs, activation_attrs, quantize_output, num_bits_weight, num_bits_activation)