class_docstr: 2D convolution that pads to keep spatial dimensions equal.
  Cannot deal with stride. Only quadratic kernels (=scalar kernel_size).
class_docstr_tok: ["2D","convolution","that","pads","to","keep","spatial","dimensions","equal",".","Can","not","deal","with","stride",".","Only","quadratic","kernels","(","=scalar","kernel_size",")","."]
code: def forward(self, x):
      return self.net(x)
  
imports: import torch
  import torch.nn
  import torchvision.utils
  from torch.nn import functional
label_desc: Use image node_id 2 for calling the Conv2dSame obj's underlying member method code with example usage: obj.forward(x) and returns: self