code: def deblockify(x, block_size: int):
      """blocks to image
      Args:
          x (Tensor): with shape (B, T, N, C) where T is number of blocks and N is sequence size per block
          block_size (int): edge length of a single square block in units of desired H, W
      """
      B, T, _, C = x.shape
      grid_size = int(math.sqrt(T))
      height = width = grid_size * block_size
      x = x.reshape(B, grid_size, grid_size, block_size, block_size, C)
      x = x.transpose(2, 3).reshape(B, height, width, C)
      return x
  
imports: import collections.abc
  import logging
  import math
  from functools import partial
  import torch
  import torch.nn.functional
  from torch import nn
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import PatchEmbed, Mlp, DropPath, create_classifier, trunc_normal_, _assert
  from timm.layers import create_conv2d, create_pool2d, to_ntuple, use_fused_attn, LayerNorm
  from ._builder import build_model_with_cfg
  from ._features_fx import register_notrace_function
  from ._manipulate import checkpoint_seq, named_apply
  from ._registry import register_model, generate_default_cfgs, register_model_deprecations
label_desc: Use image node_id 2 for calling a global function with example usage: deblockify(x, block_size) and returns: x