code: def from_pretrained(self, pretrained_gmf, pretrained_mlp, alpha):
      self.gmf.from_pretrained(pretrained_gmf)
      self.mlp.from_pretrained(pretrained_mlp)
      logit_weight = torch.cat(
          [
              alpha * self.gmf.logit.weight,
              (1.0 - alpha) * self.mlp.logit.weight,
          ],
          dim=1,
      )
      logit_bias = (
          alpha * self.gmf.logit.bias
          + (1.0 - alpha) * self.mlp.logit.bias
      )
      self.logit.weight.data.copy_(logit_weight)
      self.logit.bias.data.copy_(logit_bias)
  
imports: import torch
  import torch.nn
label_desc: Use image node_id 2 for calling the NeuMF obj's underlying member method code with example usage: obj.from_pretrained(pretrained_gmf, pretrained_mlp, alpha) without return types