code: def forward(self, x):
      x = x.float()
      output = torch.addmm(self.intercepts, x, self.coefficients)
  
      if self.regression:
          if self.loss == "log":
              return torch.exp(output)
          return output
  
      if self.binary_classification:
          indices = (output > 0).squeeze().int()
      else:
          indices = torch.argmax(output, dim=1)
      predict_res = torch.index_select(self.classes, 0, indices)
  
      if self.multi_class == "multinomial":
          output = torch.softmax(output, dim=1)
      else:
          if self.loss == "modified_huber":
              output = torch.clip(output, -1, 1)
              output += 1
              output /= 2
          else:
              output = torch.sigmoid(output)
          if not self.binary_classification:
              if self.loss == "modified_huber":
                  # This loss might assign zero to all classes, which doesn't
                  # normalize neatly; work around this to produce uniform
                  # probabilities.
                  prob_sum = torch.sum(output, dim=1, keepdim=False)
                  all_zero = prob_sum == 0
                  if torch.any(all_zero):
                      output[all_zero, :] = 1
                      prob_sum[all_zero] = len(self.classes)
                  output /= prob_sum.view((output.shape[0], -1))
              else:
                  output /= torch.sum(output, dim=1, keepdim=True)
  
      if self.binary_classification:
          output = torch.cat([1 - output, output], dim=1)
  
      return predict_res, output
  
imports: import torch
  from ._physical_operator import PhysicalOperator
label_desc: Use image node_id 2 for calling the LinearModel obj's underlying member method code with example usage: obj.forward(x) and returns: predict_res,  output, output, torch