class_docstr: RegNet shortcut, used to project the residual features to the correct size. If needed, it is also used to
  downsample the input using `stride=2`.
class_docstr_tok: ["RegNet","shortcut",",","used","to","project","the","residual","features","to","the","correct","size",".","If","needed",",","it","is","also","used","to","downsample","the","input","using","`","stride=2","`","."]
code: def forward(self, input: Tensor) -> Tensor:
      hidden_state = self.convolution(input)
      hidden_state = self.normalization(hidden_state)
      return hidden_state
  
imports: from typing import Optional
  import torch
  import torch.utils.checkpoint
  from torch import Tensor, nn
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from ...activations import ACT2FN
  from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
  from ...modeling_outputs import BaseModelOutputWithNoAttention, BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
  from ...modeling_utils import PreTrainedModel
  from ...utils import logging
  from .configuration_regnet import RegNetConfig
label_desc: Use image node_id 2 for calling the RegNetShortCut obj's underlying member method code with example usage: obj.forward(input) and returns: hidden_state