code: def forward(self, input: Tensor) -> Tensor:
      hidden_state = self.convolution(input)
      hidden_state = self.normalization(hidden_state)
      hidden_state = self.activation(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 ...modeling_outputs import BackboneOutput, BaseModelOutputWithNoAttention, BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
  from ...modeling_utils import PreTrainedModel
  from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from ...utils.backbone_utils import BackboneMixin
  from .configuration_resnet import ResNetConfig
label_desc: Use image node_id 2 for calling the ResNetConvLayer obj's underlying member method code with example usage: obj.forward(input) and returns: hidden_state