code: def _get_multiscale_mask(self, mask):
      if self.use_2d_patch:
          mask = mask.unsqueeze(0)
      output_masks = []
      for depth in self.pretrain_depth:
          size = self.feat_size[depth][-1]
          output_mask = F.interpolate(mask, size=size)
          if self.use_2d_patch:
              output_mask = output_mask[0]
          output_mask = output_mask.flatten(1).to(torch.bool)
          output_masks.append(output_mask)
      return output_masks
  
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 4 for calling the MaskMViT obj's underlying member method code with example usage: obj._get_multiscale_mask(mask) and returns: output_masks