code: def get_hf_vit_enc_layer_params(layer):
      init_ws = []
      init_bs = []
  
      init_ws.append(
          layer.attention.attention.query.weight.detach().clone()
      )
      init_bs.append(
          layer.attention.attention.query.bias.detach().clone()
      )
      init_ws.append(
          layer.attention.attention.key.weight.detach().clone()
      )
      init_bs.append(
          layer.attention.attention.key.bias.detach().clone()
      )
      init_ws.append(
          layer.attention.attention.value.weight.detach().clone()
      )
      init_bs.append(
          layer.attention.attention.value.bias.detach().clone()
      )
      init_ws.append(
          layer.attention.output.dense.weight.detach().clone()
      )
      init_bs.append(layer.attention.output.dense.bias.detach().clone())
      init_ws.append(layer.layernorm_before.weight.detach().clone())
      init_bs.append(layer.layernorm_before.bias.detach().clone())
  
      init_ws.append(layer.intermediate.dense.weight.detach().clone())
      init_bs.append(layer.intermediate.dense.bias.detach().clone())
      init_ws.append(layer.output.dense.weight.detach().clone())
      init_bs.append(layer.output.dense.bias.detach().clone())
      init_ws.append(layer.layernorm_after.weight.detach().clone())
      init_bs.append(layer.layernorm_after.bias.detach().clone())
  
      act_cmax = act_quant_config.amax.tolist()
      wei_cmax = weight_quant_config.amax.tolist()
      init_clip_max = torch.tensor([act_cmax, wei_cmax, act_cmax] * 4)
      init_ws.append(init_clip_max)
  
      return init_ws, init_bs
  
imports: import torch
  from lightseq.training.ops.pytorch.quantization import enable_quant, disable_quant, weight_quant_config, act_quant_config
label_desc: Use image node_id 2 for calling a global function with example usage: get_hf_vit_enc_layer_params(layer) and returns: init_ws,  init_bs