code: def _get_adaptive_ffn_loss(self):
      ffn_scale_factor = float(self.args.ffn_reg_scale_factor)
      filter_loss = 0
      for layer in self.encoder.sentence_encoder.layers:
          filter_loss += torch.sum(
              torch.abs(layer.fc1.weight * ffn_scale_factor)
          ) + torch.sum(torch.abs(layer.fc2.weight * ffn_scale_factor))
          filter_loss += torch.sum(
              torch.abs(layer.fc1.bias * ffn_scale_factor)
          ) + torch.sum(torch.abs(layer.fc2.bias * ffn_scale_factor))
      return filter_loss
  
imports: import logging
  import torch
  import torch.nn
  import torch.nn.functional
  from fairseq import utils
  from fairseq.models import FairseqEncoder, FairseqEncoderModel, register_model, register_model_architecture
  from fairseq.models.transformer import DEFAULT_MIN_PARAMS_TO_WRAP, TransformerEncoder
  from fairseq.modules import LayerNorm
  from fairseq.modules.quant_noise import quant_noise
  from fairseq.modules.transformer_sentence_encoder import init_bert_params
  from fairseq.utils import safe_getattr, safe_hasattr
  from .hub_interface import RobertaHubInterface
label_desc: Use image node_id 7 for calling the RobertaModel obj's underlying member method code with example usage: obj._get_adaptive_ffn_loss() and returns: filter_loss