code: def forward(self, x):
      outputs = []
  
      output = self.reduce_conv(x)
  
      for conv_i, conv in enumerate(self.convs):
          output = output if conv_i == 0 else self.pool2d(output)
          outputs.append(conv(output))
  
      return outputs
  
imports: import math
  import torch
  import numbers
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the MaxPoolDownSize obj's underlying member method code with example usage: obj.forward(x) and returns: outputs