def get_config(self):
        config = {
            'filters': self.filters,
            'kernel_size': self.kernel_size,
            'strides': self.strides,
            'padding': self.padding,
            'dilation_rate': self.dilation_rate,
            'kernel_initializer': initializers.serialize(self.kernel_initializer),
            'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
            'activity_regularizer':
                regularizers.serialize(self.activity_regularizer),
            'kernel_constraint': constraints.serialize(self.kernel_constraint),
            'demod': self.demod
        }
        base_config = super(Conv2DMod, self).get_config()
        return dict(list(base_config.items()) + list(config.items())) 

def get_config(self):
        config = {
            "groups": self.groups,
            "axis": self.axis,
            "epsilon": self.epsilon,
            "center": self.center,
            "scale": self.scale,
            "beta_initializer": initializers.serialize(self.beta_initializer),
            "gamma_initializer": initializers.serialize(self.gamma_initializer),
            "beta_regularizer": regularizers.serialize(self.beta_regularizer),
            "gamma_regularizer": regularizers.serialize(self.gamma_regularizer),
            "beta_constraint": constraints.serialize(self.beta_constraint),
            "gamma_constraint": constraints.serialize(self.gamma_constraint)
        }
        base_config = super(GroupNormalization, self).get_config()
        return dict(list(base_config.items()) + list(config.items())) 

def get_config(self):
    config = super(QDepthwiseConv2D, self).get_config()
    config.pop("filters", None)
    config.pop("kernel_initializer", None)
    config.pop("kernel_regularizer", None)
    config.pop("kernel_constraint", None)
    config["depth_multiplier"] = self.depth_multiplier
    config["depthwise_initializer"] = initializers.serialize(
        self.depthwise_initializer)
    config["depthwise_regularizer"] = regularizers.serialize(
        self.depthwise_regularizer)
    config["depthwise_constraint"] = constraints.serialize(
        self.depthwise_constraint)
    config["depthwise_quantizer"] = constraints.serialize(
        self.depthwise_quantizer_internal)
    config["bias_quantizer"] = constraints.serialize(
        self.bias_quantizer_internal)
    config["depthwise_range"] = self.depthwise_range
    config["bias_range"] = self.bias_range
    return config 

def get_config(self):
        config = {
            'groups': self.groups,
            'axis': self.axis,
            'epsilon': self.epsilon,
            'center': self.center,
            'scale': self.scale,
            'beta_initializer': initializers.serialize(self.beta_initializer),
            'gamma_initializer': initializers.serialize(self.gamma_initializer),
            'beta_regularizer': regularizers.serialize(self.beta_regularizer),
            'gamma_regularizer': regularizers.serialize(self.gamma_regularizer),
            'beta_constraint': constraints.serialize(self.beta_constraint),
            'gamma_constraint': constraints.serialize(self.gamma_constraint)
        }
        base_config = super(GroupNormalization, self).get_config()
        return dict(list(base_config.items()) + list(config.items())) 

def get_config(self):
        config = {
            "input_channels": self.input_channels,
            "output_channels": self.output_channels,
            "kernel_size": self.kernel_size,
            "strides": self.strides,
            "padding": self.padding,
            "data_format": self.data_format,
            "dilation_rate": self.dilation_rate,
            "activation": activations.serialize(self.activation),
            "groups": self.groups,
            "use_bias": self.use_bias,
            "kernel_initializer": initializers.serialize(self.kernel_initializer),
            "bias_initializer": initializers.serialize(self.bias_initializer),
            "kernel_regularizer": regularizers.serialize(self.kernel_regularizer),
            "bias_regularizer": regularizers.serialize(self.bias_regularizer),
            "activity_regularizer": regularizers.serialize(self.activity_regularizer),
            "kernel_constraint": constraints.serialize(self.kernel_constraint),
            "bias_constraint": constraints.serialize(self.bias_constraint)
        }
        base_config = super(GroupConv2D, self).get_config()
        return {**base_config, **config} 

def get_config(self):
        config = {
            "input_channels": self.input_channels,
            "output_channels": self.output_channels,
            "kernel_size": self.kernel_size,
            "strides": self.strides,
            "padding": self.padding,
            "output_padding": self.output_padding,
            "data_format": self.data_format,
            "dilation_rate": self.dilation_rate,
            "activation": activations.serialize(self.activation),
            "groups": self.groups,
            "use_bias": self.use_bias,
            "kernel_initializer": initializers.serialize(self.kernel_initializer),
            "bias_initializer": initializers.serialize(self.bias_initializer),
            "kernel_regularizer": regularizers.serialize(self.kernel_regularizer),
            "bias_regularizer": regularizers.serialize(self.bias_regularizer),
            "activity_regularizer": regularizers.serialize(self.activity_regularizer),
            "kernel_constraint": constraints.serialize(self.kernel_constraint),
            "bias_constraint": constraints.serialize(self.bias_constraint)
        }
        base_config = super(GroupConv2DTranspose, self).get_config()
        return {**base_config, **config} 

def get_config(self):
        config = {
            'axis': self.axis,
            'epsilon': self.epsilon,
            'beta_initializer': initializers.serialize(self.beta_initializer),
            'tau_initializer': initializers.serialize(self.tau_initializer),
            'gamma_initializer': initializers.serialize(self.gamma_initializer),
            'beta_regularizer': regularizers.serialize(self.beta_regularizer),
            'tau_regularizer': regularizers.serialize(self.tau_regularizer),
            'gamma_regularizer': regularizers.serialize(self.gamma_regularizer),
            'beta_constraint': constraints.serialize(self.beta_constraint),
            'gamma_constraint': constraints.serialize(self.gamma_constraint),
            'tau_constraint': constraints.serialize(self.tau_constraint)
        }
        base_config = super(FRN, self).get_config()
        return dict(list(base_config.items()) + list(config.items())) 

def serialize_kwarg(key, attr):
    if key.endswith('_initializer'):
        return initializers.serialize(attr)
    if key.endswith('_regularizer'):
        return regularizers.serialize(attr)
    if key.endswith('_constraint'):
        return constraints.serialize(attr)
    if key == 'activation':
        return activations.serialize(attr)
    if key == 'use_bias':
        return attr 

def get_config(self):
        config = {
            'ratio': self.ratio,
            'return_mask': self.return_mask,
            'kernel_initializer': initializers.serialize(self.kernel_initializer),
            'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
            'kernel_constraint': constraints.serialize(self.kernel_constraint),
        }
        base_config = super().get_config()
        return dict(list(base_config.items()) + list(config.items())) 

def get_config(self):
        config = {
            'k': self.k,
            'channels': self.channels,
            'return_mask': self.return_mask,
            'kernel_initializer': initializers.serialize(self.kernel_initializer),
            'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
            'kernel_constraint': constraints.serialize(self.kernel_constraint),
        }
        base_config = super().get_config()
        return dict(list(base_config.items()) + list(config.items())) 

def get_config(self):
        config = {
            'k': self.k,
            'mlp_hidden': self.mlp_hidden,
            'mlp_activation': self.mlp_activation,
            'return_mask': self.return_mask,
            'kernel_initializer': initializers.serialize(self.kernel_initializer),
            'bias_initializer': initializers.serialize(self.bias_initializer),
            'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
            'bias_regularizer': regularizers.serialize(self.bias_regularizer),
            'kernel_constraint': constraints.serialize(self.kernel_constraint),
            'bias_constraint': constraints.serialize(self.bias_constraint)
        }
        base_config = super().get_config()
        return dict(list(base_config.items()) + list(config.items())) 

def get_config(self):
        config = {
            'channels': self.channels,
            'activation': activations.serialize(self.activation),
            'use_bias': self.use_bias,
            'kernel_initializer': initializers.serialize(self.kernel_initializer),
            'bias_initializer': initializers.serialize(self.bias_initializer),
            'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
            'bias_regularizer': regularizers.serialize(self.bias_regularizer),
            'kernel_constraint': constraints.serialize(self.kernel_constraint),
            'bias_constraint': constraints.serialize(self.bias_constraint)
        }
        base_config = super().get_config()
        return dict(list(base_config.items()) + list(config.items())) 

def get_config(self):
        config = super(DepthwiseConv2D, self).get_config()
        config.pop('filters')
        config.pop('kernel_initializer')
        config.pop('kernel_regularizer')
        config.pop('kernel_constraint')
        config['depth_multiplier'] = self.depth_multiplier
        config['depthwise_initializer'] = initializers.serialize(self.depthwise_initializer)
        config['depthwise_regularizer'] = regularizers.serialize(self.depthwise_regularizer)
        config['depthwise_constraint'] = constraints.serialize(self.depthwise_constraint)
        return config 

def get_config(self):
    config = {
        "units": self.units,
        "activation": activations.serialize(self.activation),
        "use_bias": self.use_bias,
        "kernel_quantizer":
            constraints.serialize(self.kernel_quantizer_internal),
        "bias_quantizer":
            constraints.serialize(self.bias_quantizer_internal),
        "kernel_initializer":
            initializers.serialize(self.kernel_initializer),
        "bias_initializer":
            initializers.serialize(self.bias_initializer),
        "kernel_regularizer":
            regularizers.serialize(self.kernel_regularizer),
        "bias_regularizer":
            regularizers.serialize(self.bias_regularizer),
        "activity_regularizer":
            regularizers.serialize(self.activity_regularizer),
        "kernel_constraint":
            constraints.serialize(self.kernel_constraint),
        "bias_constraint":
            constraints.serialize(self.bias_constraint),
        "kernel_range": self.kernel_range,
        "bias_range": self.bias_range
    }
    base_config = super(QDense, self).get_config()
    return dict(list(base_config.items()) + list(config.items())) 

def get_config(self):
    config = {
        "kernel_quantizer":
            constraints.serialize(self.kernel_quantizer_internal),
        "bias_quantizer":
            constraints.serialize(self.bias_quantizer_internal),
        "kernel_range": self.kernel_range,
        "bias_range": self.bias_range
    }
    base_config = super(QConv1D, self).get_config()
    return dict(list(base_config.items()) + list(config.items())) 

def get_config(self):
    config = {
        "kernel_quantizer":
            constraints.serialize(self.kernel_quantizer_internal),
        "bias_quantizer":
            constraints.serialize(self.bias_quantizer_internal),
        "kernel_range": self.kernel_range,
        "bias_range": self.bias_range
    }
    base_config = super(QConv2D, self).get_config()
    return dict(list(base_config.items()) + list(config.items())) 

def get_config(self):
    config = {
        'axis': self.axis,
        'momentum': self.momentum,
        'epsilon': self.epsilon,
        'center': self.center,
        'scale': self.scale,
        'beta_quantizer':
            constraints.serialize(self.beta_quantizer_internal),
        'gamma_quantizer':
            constraints.serialize(self.gamma_quantizer_internal),
        'mean_quantizer':
            constraints.serialize(self.mean_quantizer_internal),
        'variance_quantizer':
            constraints.serialize(self.variance_quantizer_internal),
        'beta_initializer': initializers.serialize(self.beta_initializer),
        'gamma_initializer': initializers.serialize(self.gamma_initializer),
        'moving_mean_initializer':
            initializers.serialize(self.moving_mean_initializer),
        'moving_variance_initializer':
            initializers.serialize(self.moving_variance_initializer),
        'beta_regularizer': regularizers.serialize(self.beta_regularizer),
        'gamma_regularizer': regularizers.serialize(self.gamma_regularizer),
        'beta_constraint': constraints.serialize(self.beta_constraint),
        'gamma_constraint': constraints.serialize(self.gamma_constraint),
        'beta_range': self.beta_range,
        'gamma_range': self.gamma_range,
    }
    base_config = super(BatchNormalization, self).get_config()
    return dict(list(base_config.items()) + list(config.items())) 

def get_config(self):
        config = {"T": self.T,
                  "n_hidden": self.n_hidden,
                  "activation": activations.serialize(self.activation),
                  "activation_lstm": activations.serialize(
                      self.activation_lstm),
                  "recurrent_activation": activations.serialize(
                      self.recurrent_activation),
                  "kernel_initializer": initializers.serialize(
                      self.kernel_initializer),
                  "recurrent_initializer": initializers.serialize(
                      self.recurrent_initializer),
                  "bias_initializer": initializers.serialize(
                      self.bias_initializer),
                  "use_bias": self.use_bias,
                  "unit_forget_bias": self.unit_forget_bias,
                  "kernel_regularizer": regularizers.serialize(
                      self.kernel_regularizer),
                  "recurrent_regularizer": regularizers.serialize(
                      self.recurrent_regularizer),
                  "bias_regularizer": regularizers.serialize(
                      self.bias_regularizer),
                  "kernel_constraint": constraints.serialize(
                      self.kernel_constraint),
                  "recurrent_constraint": constraints.serialize(
                      self.recurrent_constraint),
                  "bias_constraint": constraints.serialize(self.bias_constraint)

                  }
        base_config = super().get_config()
        return dict(list(base_config.items()) + list(config.items())) 

def get_config(self) -> Dict:
        """
        Part of keras layer interface, where the signature is converted into a dict
        Returns:
            configurational dictionary
        """
        config = {
            'activation': activations.serialize(self.activation),
            'use_bias': self.use_bias,
            'kernel_initializer': initializers.serialize(
                self.kernel_initializer),
            'bias_initializer': initializers.serialize(
                self.bias_initializer),
            'kernel_regularizer': regularizers.serialize(
                self.kernel_regularizer),
            'bias_regularizer': regularizers.serialize(
                self.bias_regularizer),
            'activity_regularizer': regularizers.serialize(
                self.activity_regularizer),
            'kernel_constraint': constraints.serialize(
                self.kernel_constraint),
            'bias_constraint': constraints.serialize(self.bias_constraint)
        }

        base_config = super().get_config()
        return dict(list(base_config.items()) + list(config.items()))  # noqa 

def get_config(self):
        config = {
            "alpha_initializer": initializers.serialize(self.b_initializer),
            "alpha_regularizer": regularizers.serialize(self.b_regularizer),
            "alpha_constraint": constraints.serialize(self.b_constraint),
            "b_initializer": initializers.serialize(self.b_initializer),
            "b_regularizer": regularizers.serialize(self.b_regularizer),
            "b_constraint": constraints.serialize(self.b_constraint),
            "shared_axes": self.shared_axes,
        }
        base_config = super(APL, self).get_config()
        return dict(list(base_config.items()) + list(config.items()))