class_docstr: QuantizeConfig for non recurrent Keras layers.
class_docstr_tok: ["QuantizeConfig","for","non","recurrent","Keras","layers","."]
code: def __eq__(self, other):
      if not isinstance(other, DefaultNBitQuantizeConfig):
          return False
  
      return (
          self.weight_attrs == other.weight_attrs
          and self.activation_attrs == self.activation_attrs
          and self.weight_quantizer == other.weight_quantizer
          and self.activation_quantizer == other.activation_quantizer
          and self.quantize_output == other.quantize_output
      )
  
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 9 for calling the DefaultNBitQuantizeConfig obj's underlying member method code with example usage: obj.__eq__(other) and returns: unknown, False