class_docstr: ### Two $3  imes 3$ Convolution Layers
  
  Each step in the contraction path and expansive path have two $3    imes 3$
  convolutional layers followed by ReLU activations.
  
  In the U-Net paper they used $0$ padding,
  but we use $1$ padding so that final feature map is not cropped.
class_docstr_tok: ["#","#","#","Two","$","3","imes","3","$","Convolution","Layers","Each","step","in","the","contraction","path","and","expansive","path","have","two","$","3","imes","3","$","convolutional","layers","followed","by","ReLU","activations",".","In","the","U-Net","paper","they","used","$","0","$","padding",",","but","we","use","$","1","$","padding","so","that","final","feature","map","is","not","cropped","."]
code: def forward(self, x: torch.Tensor):
      # Apply the two convolution layers and activations
      x = self.first(x)
      x = self.act1(x)
      x = self.second(x)
      return self.act2(x)
  
imports: import torch
  import torchvision.transforms.functional
  from torch import nn
label_desc: Use image node_id 2 for calling the DoubleConvolution obj's underlying member method code with example usage: obj.forward(x) and returns: self