code: def forward(self, samples: NestedTensor):
      if isinstance(samples, (list, torch.Tensor)):
          samples = nested_tensor_from_tensor_list(samples)
      features, pos = self.detr.backbone(samples)
  
      bs = features[-1].tensors.shape[0]
  
      src, mask = features[-1].decompose()
      assert mask is not None
      src_proj = self.detr.input_proj(src)
      hs, memory = self.detr.transformer(
          src_proj, mask, self.detr.query_embed.weight, pos[-1]
      )
  
      outputs_class = self.detr.class_embed(hs)
      outputs_coord = self.detr.bbox_embed(hs).sigmoid()
      out = {
          "pred_logits": outputs_class[-1],
          "pred_boxes": outputs_coord[-1],
      }
      if self.detr.aux_loss:
          out["aux_outputs"] = self.detr._set_aux_loss(
              outputs_class, outputs_coord
          )
  
      # FIXME h_boxes takes the last one computed, keep this in mind
      bbox_mask = self.bbox_attention(hs[-1], memory, mask=mask)
  
      seg_masks = self.mask_head(
          src_proj,
          bbox_mask,
          [
              features[2].tensors,
              features[1].tensors,
              features[0].tensors,
          ],
      )
      outputs_seg_masks = seg_masks.view(
          bs,
          self.detr.num_queries,
          seg_masks.shape[-2],
          seg_masks.shape[-1],
      )
  
      out["pred_masks"] = outputs_seg_masks
      return out
  
imports: import io
  from collections import defaultdict
  from typing import List, Optional
  import torch
  import torch.nn
  import torch.nn.functional
  from torch import Tensor
  from PIL import Image
  import util.box_ops
  from util.misc import NestedTensor, interpolate, nested_tensor_from_tensor_list
label_desc: Use image node_id 2 for calling the DETRsegm obj's underlying member method code with example usage: obj.forward(samples) and returns: out