class_docstr: Single hierarchical level of a Nested Transformer
      
class_docstr_tok: ["Single","hierarchical","level","of","a","Nested","Transformer"]
docstr: expects x as (B, C, H, W)
code: def forward(self, x):
      """
      expects x as (B, C, H, W)
      """
      x = self.pool(x)
      x = x.permute(
          0, 2, 3, 1
      )  # (B, H', W', C), switch to channels last for transformer
      x = blockify(x, self.block_size)  # (B, T, N, C')
      x = x + self.pos_embed
      if self.grad_checkpointing and not torch.jit.is_scripting():
          x = checkpoint_seq(self.transformer_encoder, x)
      else:
          x = self.transformer_encoder(x)  # (B, T, N, C')
      x = deblockify(x, self.block_size)  # (B, H', W', C')
      # Channel-first for block aggregation, and generally to replicate convnet feature map at each stage
      return x.permute(0, 3, 1, 2)
  
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 the NestLevel obj's underlying member method code with example usage: obj.forward(x) and returns: x