code: def forward(self, x: torch.Tensor) -> torch.Tensor:
      if self.rank == 1:
          (
              batch_size_shape,
              channel_in_shape,
              dim_x,
          ) = x.shape  # pylint: disable=unused-variable
          xx_range = torch.arange(dim_x, dtype=torch.int32)
          xx_channel = xx_range[None, None, :]
  
          xx_channel = xx_channel.float() / (dim_x - 1)
          xx_channel = xx_channel * 2 - 1
          xx_channel = xx_channel.repeat(batch_size_shape, 1, 1)
  
          xx_channel = xx_channel.to(x.device)
          out = torch.cat([x, xx_channel], dim=1)
  
          if self.with_r:
              rr = torch.sqrt(torch.pow(xx_channel - 0.5, 2))
              out = torch.cat([out, rr], dim=1)
  
      elif self.rank == 2:
          batch_size_shape, channel_in_shape, dim_y, dim_x = x.shape
          xx_ones = torch.ones([1, 1, 1, dim_x], dtype=torch.int32)
          yy_ones = torch.ones([1, 1, 1, dim_y], dtype=torch.int32)
  
          xx_range = torch.arange(dim_y, dtype=torch.int32)
          yy_range = torch.arange(dim_x, dtype=torch.int32)
          xx_range = xx_range[None, None, :, None]
          yy_range = yy_range[None, None, :, None]
  
          xx_channel = torch.matmul(xx_range, xx_ones)
          yy_channel = torch.matmul(yy_range, yy_ones)
  
          # transpose y
          yy_channel = yy_channel.permute(0, 1, 3, 2)
  
          xx_channel = xx_channel.float() / (dim_y - 1)
          yy_channel = yy_channel.float() / (dim_x - 1)
  
          xx_channel = xx_channel * 2 - 1
          yy_channel = yy_channel * 2 - 1
  
          xx_channel = xx_channel.repeat(batch_size_shape, 1, 1, 1)
          yy_channel = yy_channel.repeat(batch_size_shape, 1, 1, 1)
  
          xx_channel = xx_channel.to(x.device)
          yy_channel = yy_channel.to(x.device)
  
          out = torch.cat([x, xx_channel, yy_channel], dim=1)
  
          if self.with_r:
              rr = torch.sqrt(
                  torch.pow(xx_channel - 0.5, 2)
                  + torch.pow(yy_channel - 0.5, 2)
              )
              out = torch.cat([out, rr], dim=1)
  
      elif self.rank == 3:
          (
              batch_size_shape,
              channel_in_shape,
              dim_z,
              dim_y,
              dim_x,
          ) = x.shape
          xx_ones = torch.ones([1, 1, 1, 1, dim_x], dtype=torch.int32)
          yy_ones = torch.ones([1, 1, 1, 1, dim_y], dtype=torch.int32)
          zz_ones = torch.ones([1, 1, 1, 1, dim_z], dtype=torch.int32)
  
          xy_range = torch.arange(dim_y, dtype=torch.int32)
          xy_range = xy_range[None, None, None, :, None]
  
          yz_range = torch.arange(dim_z, dtype=torch.int32)
          yz_range = yz_range[None, None, None, :, None]
  
          zx_range = torch.arange(dim_x, dtype=torch.int32)
          zx_range = zx_range[None, None, None, :, None]
  
          xy_channel = torch.matmul(xy_range, xx_ones)
          xx_channel = torch.cat(
              [xy_channel + i for i in range(dim_z)], dim=2
          )
  
          yz_channel = torch.matmul(yz_range, yy_ones)
          yz_channel = yz_channel.permute(0, 1, 3, 4, 2)
          yy_channel = torch.cat(
              [yz_channel + i for i in range(dim_x)], dim=4
          )
  
          zx_channel = torch.matmul(zx_range, zz_ones)
          zx_channel = zx_channel.permute(0, 1, 4, 2, 3)
          zz_channel = torch.cat(
              [zx_channel + i for i in range(dim_y)], dim=3
          )
  
          xx_channel = xx_channel.to(x.device)
          yy_channel = yy_channel.to(x.device)
          zz_channel = zz_channel.to(x.device)
          out = torch.cat(
              [x, xx_channel, yy_channel, zz_channel], dim=1
          )
  
          if self.with_r:
              rr = torch.sqrt(
                  torch.pow(xx_channel - 0.5, 2)
                  + torch.pow(yy_channel - 0.5, 2)
                  + torch.pow(zz_channel - 0.5, 2)
              )
              out = torch.cat([out, rr], dim=1)
      else:
          raise NotImplementedError
  
      return out
  
imports: from typing import Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn.utils import parametrize
  from TTS.tts.layers.delightful_tts.kernel_predictor import KernelPredictor
label_desc: Use image node_id 2 for calling the AddCoords obj's underlying member method code with example usage: obj.forward(x) and returns: out