Quantized threshold voltage

[Maya7991]

I have written a SNN architecture with Quantization aware training using Brevitas library. Given below is my SNN class. I am trying to do a RTL simulation for SNN core and want to get the quantized weights and threshold voltage of my model. I have saved the quantized model weights. However, I couldn't figure out the quantized threshold voltage for the SNN. I understand that it will be default value of 1 in SnnTorch but will it change when I have a quantized network with weights of int8? I need this data to be able to do the RTL simulation.

# neuron and simulation parameters
spike_grad = surrogate.fast_sigmoid(slope=25)
beta = 0.5

class SnnNet(nn.Module):
  def __init__(self,num_steps,in_channels=16,out_channels=32, use_pool=False):
    super(SnnNet,self).__init__()
    self.use_pool = use_pool
    self.num_steps = num_steps

    qlif = quant.state_quant(num_bits=8, uniform=False, thr_centered=True)

    # self.conv = nn.Conv2d(in_channels,out_channels,kernel_size=3,padding=1,stride=1)
    self.conv = qnn.QuantConv2d(in_channels, out_channels, kernel_size=3,padding=1,stride=1, bias=True, weight_bit_width=8)
    self.lif1 = snn.Leaky(beta=beta, state_quant=qlif)
    self.pool = nn.MaxPool2d(kernel_size=2,stride=2)
    self.activation = snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True)

  def forward(self,data):
    self.activation.reset_mem()
    out = []
    for step in range(self.num_steps):
      output = self.conv(data[step])
      spike_out = self.activation(output)

      if self.use_pool:
        spike_out = self.pool(spike_out);
      out.append(spike_out)

    return torch.stack(out,dim=0)

I am assuming, getting quantized threshold voltage has to do with SnnTorch than Brevitas. Hence posting my query here. If not, I apologize in advance but please let me know so that I can direct my questions to Brevitas.