class_docstr: Simple pre-activated ResNet-style block.
  
  Structure: input -> ReLU -> Conv2d -> ReLU -> Conv2d + input
  
  Args:
      in_channels: Number of input channels.
      out_channels: Number of output channels.
      stride: Stride of convolutions.
class_docstr_tok: ["Simple","pre-activated","ResNet-style","block",".","Structure",":","input","-",">","ReLU","-",">","Conv2d","-",">","ReLU","-",">","Conv2d","+","input","Args",":","in_channels",":","Number","of","input","channels",".","out_channels",":","Number","of","output","channels",".","stride",":","Stride","of","convolutions","."]
code: def forward(self, image: Tensor) -> Tensor:
      return image + self.block(image)
  
imports: from typing import Any, List, NamedTuple, Optional, Sequence, Tuple
  import torch
  import torch.nn.functional
  from compressai.entropy_models import EntropyBottleneck
  from torch import Tensor, nn
  import neuralcompression.functional
  from neuralcompression.layers import SimplifiedGDN, SimplifiedInverseGDN
label_desc: Use image node_id 2 for calling the _ResidualBlock obj's underlying member method code with example usage: obj.forward(image) and returns: unknown