code: def forward(self, input: Tensor) -> Tensor:
      result = self.layer_scale * self.block(input)
      result = self.stochastic_depth(result)
      result += input
      return result
  
imports: from functools import partial
  from typing import Any, Callable, List, Optional, Sequence
  import torch
  from torch import nn, Tensor
  from torch.nn import functional
  from ..ops.misc import Conv2dNormActivation, Permute
  from ..ops.stochastic_depth import StochasticDepth
  from ..transforms._presets import ImageClassification
  from ..utils import _log_api_usage_once
  from ._api import register_model, Weights, WeightsEnum
  from ._meta import _IMAGENET_CATEGORIES
  from ._utils import _ovewrite_named_param, handle_legacy_interface
label_desc: Use image node_id 2 for calling the CNBlock obj's underlying member method code with example usage: obj.forward(input) and returns: result