def METHOD_NAME(self):
    re_str = ''
    re_str += '<UniDeepFsmn> %d %d\n'\
              % (self.output_dim, self.input_dim)
    re_str += '<LearnRateCoef> %d <HidSize> %d <LOrder> %d <LStride> %d <MaxNorm> 0\n'\
              % (1, self.hidden_size, self.lorder, 1)
    lfiters = self.state_dict()['conv1.weight']
    x = np.flipud(lfiters.squeeze().numpy().T)
    re_str += to_kaldi_matrix(x)
    proj_weights = self.state_dict()['project.weight']
    x = proj_weights.squeeze().numpy()
    re_str += to_kaldi_matrix(x)
    linear_weights = self.state_dict()['linear.weight']
    x = linear_weights.squeeze().numpy()
    re_str += to_kaldi_matrix(x)
    linear_bias = self.state_dict()['linear.bias']
    x = linear_bias.squeeze().numpy()
    re_str += to_kaldi_matrix(x)
    return re_str