code: def _get_pixel_label_2d(self, input_img, output_masks, norm=True):
      input_img = input_img.permute(0, 2, 3, 1)
      labels = []
      for depth, output_mask in zip(self.pretrain_depth, output_masks):
          size = self.feat_stride[depth][-1]
          label = input_img.unfold(1, size, size).unfold(2, size, size)
          label = label.flatten(1, 2).flatten(2)
          label = label[output_mask]
          if norm:
              mean = label.mean(dim=-1, keepdim=True)
              var = label.var(dim=-1, keepdim=True)
              label = (label - mean) / (var + 1.0e-6) ** 0.5
          labels.append(label)
      return labels
  
imports: import math
  from functools import partial
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn.init import trunc_normal_
  import slowfast.utils.logging
  import slowfast.utils.misc
  from slowfast.models import head_helper
  from slowfast.models.attention import attention_pool
  from slowfast.models.utils import calc_mvit_feature_geometry
  from slowfast.models.video_model_builder import MViT
  from .None import head_helper, operators, resnet_helper, stem_helper
  from .build import MODEL_REGISTRY
label_desc: Use image node_id 7 for calling the MaskMViT obj's underlying member method code with example usage: obj._get_pixel_label_2d(input_img, output_masks, norm) and returns: labels