code: def forward(self, inputs):
      if self.padding[1] != 0 and self.causal:
          inputs = F.pad(inputs, [self.padding[1], 0, 0, 0])
      else:
          inputs = F.pad(
              inputs, [self.padding[1], self.padding[1], 0, 0]
          )
  
      if self.complex_axis == 0:
          real = self.real_conv(inputs)
          imag = self.imag_conv(inputs)
          real2real, imag2real = torch.chunk(real, 2, self.complex_axis)
          real2imag, imag2imag = torch.chunk(imag, 2, self.complex_axis)
  
      else:
          if isinstance(inputs, torch.Tensor):
              real, imag = torch.chunk(inputs, 2, self.complex_axis)
  
          real2real = self.real_conv(
              real,
          )
          imag2imag = self.imag_conv(
              imag,
          )
  
          real2imag = self.imag_conv(real)
          imag2real = self.real_conv(imag)
  
      real = real2real - imag2imag
      imag = real2imag + imag2real
      out = torch.cat([real, imag], self.complex_axis)
  
      return out
  
imports: import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the ComplexConv2d obj's underlying member method code with example usage: obj.forward(inputs) and returns: out