docstr: ComplexConvTranspose2d.
  
  in_channels: real+imag
  out_channels: real+imag
docstr_tok: ["ComplexConvTranspose2d",".","in_channels",":","real+imag","out_channels",":","real+imag"]
code: def __init__(
      self,
      in_channels,
      out_channels,
      kernel_size=(1, 1),
      stride=(1, 1),
      padding=(0, 0),
      output_padding=(0, 0),
      causal=False,
      complex_axis=1,
      groups=1,
  ):
      """ComplexConvTranspose2d.
  
      in_channels: real+imag
      out_channels: real+imag
      """
      super(ComplexConvTranspose2d, self).__init__()
      self.in_channels = in_channels // 2
      self.out_channels = out_channels // 2
      self.kernel_size = kernel_size
      self.stride = stride
      self.padding = padding
      self.output_padding = output_padding
      self.groups = groups
  
      self.real_conv = nn.ConvTranspose2d(
          self.in_channels,
          self.out_channels,
          kernel_size,
          self.stride,
          padding=self.padding,
          output_padding=output_padding,
          groups=self.groups,
      )
      self.imag_conv = nn.ConvTranspose2d(
          self.in_channels,
          self.out_channels,
          kernel_size,
          self.stride,
          padding=self.padding,
          output_padding=output_padding,
          groups=self.groups,
      )
      self.complex_axis = complex_axis
  
      nn.init.normal_(self.real_conv.weight, std=0.05)
      nn.init.normal_(self.imag_conv.weight, std=0.05)
      nn.init.constant_(self.real_conv.bias, 0.0)
      nn.init.constant_(self.imag_conv.bias, 0.0)
  
imports: import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new ComplexConvTranspose2d object from inherited base classes: nn with example: obj = ComplexConvTranspose2d(in_channels, out_channels, kernel_size, stride, padding, output_padding, causal, complex_axis, groups)