code: def _patchify(self, imgs, p=16, time_stride_loss=True):
      N, _, T, H, W = imgs.shape
      u = 1 if time_stride_loss else self.patch_stride[0]
      assert H == W and H % p == 0 and T % u == 0
      h = w = H // p
      t = T // u
      x = imgs.reshape(shape=(N, 3, t, u, h, p, w, p))
      x = torch.einsum("nctuhpwq->nthwupqc", x)
      x = x.reshape(shape=(N, t * h * w, u * p**2 * 3))
      self.patch_info = (N, T, H, W, p, u, t, h, w)
      return x
  
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 5 for calling the MaskMViT obj's underlying member method code with example usage: obj._patchify(imgs, p, time_stride_loss) and returns: x