How to use the hls4ml.model.hls_model.register_layer function in hls4ml

params = self._default_config_params()
        params['n_in'] = self.get_input_variable().size_cpp()

        return self._config_template.format(**params)

batchnorm_quantized_tanh_config_template = """struct config{index} : nnet::batchnorm_quantized_tanh_config {{
    static const unsigned n_in = {n_in};
    static const unsigned n_filt = {n_filt};
    static const unsigned io_type = nnet::{iotype};
    static const unsigned reuse_factor = {reuse};
}};\n"""

batchnorm_quantized_tanh_function_template = 'nnet::normalize_{quantize}_tanh<{input_t}, {config}>({input}, {output}, {threshold});'

# Register the layer types to the layer map
hls_model.register_layer('BatchNormalizationQuantizedTanh', BatchNormalizationQuantizedTanh)

# Register the templates for config and function
templates.register_templates('BatchNormalizationQuantizedTanh', batchnorm_quantized_tanh_function_template, batchnorm_quantized_tanh_config_template)

class MergeBatchNormAndQuantizedTanh(OptimizerPass):
    def match(self, node):
        is_match = (node.__class__.__name__ == 'Activation'
            and node.get_attr('activation') in ['binary_tanh', 'ternary_tanh']
            and node.get_input_node().__class__.__name__ == 'BatchNormalization')
        return is_match

    def transform(self, model, node):
        bn_layer = node.get_input_node()
        # Remove the Activation layer
        model.remove_node(node, rewire=True)
        # Make a new layer with the new attributes