original_docstring: 
          Creates a transpose convolution module and initialize the weights with Xavier intitialization
          
docstring: Creates a transpose convolution module and initialize the weights with Xavier intitialization
docstring_tokens: Creates a transpose convolution module and initialize the weights with Xavier intitialization
code: def _conv_transpose(self, in_channels, out_channels, weight_init=True):
          """
          Creates a transpose convolution module and initialize the weights with Xavier intitialization
          """
          if self.is_3D:
              convT = nn.ConvTranspose3d(in_channels, out_channels, kernel_size=(1, 3, 3), stride=(1, 2, 2),
                                         padding=(0, 1, 1), output_padding=(0, 1, 1))
          else:
              convT = nn.ConvTranspose2d(in_channels, out_channels, kernel_size=(3, 3), stride=(2, 2),
                                         padding=(1, 1), output_padding=(1, 1))
          if weight_init:
              convT.weight.data = nn.init.xavier_normal_(convT.weight.data)
          return convT
code_tokens: def _conv_transpose ( self , in_channels , out_channels , weight_init = True ) : if self . is_3D : convT = nn . ConvTranspose3d ( in_channels , out_channels , kernel_size = ( 1 , 3 , 3 ) , stride = ( 1 , 2 , 2 ) , padding = ( 0 , 1 , 1 ) , output_padding = ( 0 , 1 , 1 ) ) else : convT = nn . ConvTranspose2d ( in_channels , out_channels , kernel_size = ( 3 , 3 ) , stride = ( 2 , 2 ) , padding = ( 1 , 1 ) , output_padding = ( 1 , 1 ) ) if weight_init : convT . weight . data = nn . init . xavier_normal_ ( convT . weight . data ) return convT
short_docstring: Creates a transpose convolution module and initialize the weights with Xavier intitialization
short_docstring_tokens: Creates a transpose convolution module and initialize the weights with Xavier intitialization
comment: """
          Creates a transpose convolution module and initialize the weights with Xavier intitialization
          """