class_docstr: A module which applies the following actions:
      - convolution with 3x3 kernel;
      - batch normalization (if enabled);
      - ELU.
  Attributes:
      in_ch: Number of input channels.
      out_ch: Number of output channels.
      bn: A boolean indicating if Batch Normalization is enabled or not.
class_docstr_tok: ["A","module","which","applies","the","following","actions",":","-","convolution","with","3x3","kernel",";","-","batch","normalization","(","if","enabled",")",";","-","ELU",".","Attributes",":","in_ch",":","Number","of","input","channels",".","out_ch",":","Number","of","output","channels",".","bn",":","A","boolean","indicating","if","Batch","Normalization","is","enabled","or","not","."]
code: def forward(self, x):
      x = self.conv(x)
      if self.bn is not None:
          x = self.bn(x)
      x = self.activation(x)
      return x
  
imports: import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the Conv3BN obj's underlying member method code with example usage: obj.forward(x) and returns: x