code: def forward(self, x):
      x = self.downsample(x)
      if self.grad_checkpointing and not torch.jit.is_scripting():
          x = checkpoint_seq(self.blocks, x)
      else:
          x = self.blocks(x)
      return x
  
imports: import math
  from collections import OrderedDict
  from functools import partial
  from typing import Tuple
  import torch
  import torch.nn.functional
  from torch import nn
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import trunc_normal_tf_, DropPath, LayerNorm2d, Mlp, SelectAdaptivePool2d, create_conv2d, use_fused_attn, NormMlpClassifierHead, ClassifierHead
  from ._builder import build_model_with_cfg
  from ._features_fx import register_notrace_module
  from ._manipulate import named_apply, checkpoint_seq
  from ._registry import register_model, generate_default_cfgs
label_desc: Use image node_id 2 for calling the EdgeNeXtStage obj's underlying member method code with example usage: obj.forward(x) and returns: x