code: def forward(self, priors, processed_feat):
      x = self.fc(processed_feat)
      x = self.bn(x)
      x = torch.mul(x, priors)
      x = self.selector(x)
      return x
  
imports: import torch
  from torch.nn import Linear, BatchNorm1d, ReLU
  import numpy
  from pytorch_tabnet import sparsemax
label_desc: Use image node_id 2 for calling the AttentiveTransformer obj's underlying member method code with example usage: obj.forward(priors, processed_feat) and returns: x