code: def forward(self, features):
      coord_features = self.compute_coordinates(features)
      features = torch.cat([coord_features, features], dim=1)
      pred_logits, pred_kernel, pred_scores, iam = self.inst_branch(
          features
      )
      mask_features = self.mask_branch(features)
  
      N = pred_kernel.shape[1]
      # mask_features: BxCxHxW
      B, C, H, W = mask_features.shape
      pred_masks = torch.bmm(
          pred_kernel, mask_features.view(B, C, H * W)
      ).view(B, N, H, W)
  
      pred_masks = F.interpolate(
          pred_masks,
          scale_factor=self.scale_factor,
          mode="bilinear",
          align_corners=False,
      )
  
      output = {
          "pred_logits": pred_logits,
          "pred_masks": pred_masks,
          "pred_scores": pred_scores,
      }
  
      if self.output_iam:
          iam = F.interpolate(
              iam,
              scale_factor=self.scale_factor,
              mode="bilinear",
              align_corners=False,
          )
          output["pred_iam"] = iam
  
      return output
  
imports: import math
  import torch
  import torch.nn
  import torch.nn.functional
  from mmengine.model.weight_init import caffe2_xavier_init, kaiming_init
  from torch.nn import init
  from mmdet.registry import MODELS
label_desc: Use image node_id 4 for calling the BaseIAMDecoder obj's underlying member method code with example usage: obj.forward(features) and returns: output