code: def predict(self, features, batch_data_samples):
      # multi scale feature
      mask_features, multi_scale_features = self.pixel_decoder(features)
  
      # pre process
      extra, all_text_prompts, num_thing_class = self.pre_process(
          batch_data_samples, mask_features.device
      )
  
      # transformer decoder forward
      predictions = self.predictor(
          multi_scale_features, mask_features, extra=extra
      )
  
      # post process
      return self.post_process(
          predictions,
          batch_data_samples,
          all_text_prompts,
          num_thing_class,
      )
  
file_path: mmdetection/projects/XDecoder/xdecoder/unified_head.py
imports: import copy
  from typing import Sequence
  import torch
  from mmengine.structures import InstanceData, PixelData
  from torch import nn
  from torch.nn import functional
  from mmdet.evaluation.functional import INSTANCE_OFFSET
  from mmdet.registry import MODELS
  from .utils import is_lower_torch_version, retry_if_cuda_oom, sem_seg_postprocess
label_desc: Use image node_id 3 for calling the XDecoderUnifiedhead obj's underlying member method code with example usage: obj.predict(features, batch_data_samples) and returns: self