code: def conv_general_dilated(
      x: paddle.Tensor,
      filters: paddle.Tensor,
      strides: Union[
          int, Tuple[int], Tuple[int, int], Tuple[int, int, int]
      ],
      padding: Union[str, int, Sequence[Tuple[int, int]]],
      /,
      *,
      dims: Optional[int] = 2,
      data_format: Optional[str] = "channel_last",
      filter_format: Optional[str] = "channel_last",
      feature_group_count: Optional[int] = 1,
      x_dilations: Optional[
          Union[int, Tuple[int], Tuple[int, int], Tuple[int, int, int]]
      ] = 1,
      dilations: Optional[
          Union[int, Tuple[int], Tuple[int, int], Tuple[int, int, int]]
      ] = 1,
      bias: Optional[paddle.Tensor] = None,
      out: Optional[paddle.Tensor] = None,
  ):
      if data_format == "channel_first":
          x = paddle.transpose(x, perm=(0, *range(2, dims + 2), 1))
  
      if filter_format == "channel_first":
          filters = paddle.transpose(
              filters, (*range(2, dims + 2), 1, 0)
          )
  
      # adding dilation in input
      x_dilations = (
          [x_dilations] * dims
          if isinstance(x_dilations, int)
          else x_dilations
      )
      for i in range(dims):
          if x_dilations[i] > 1:
              h = x.shape[1 + i]
              new_height = h + (h - 1) * (x_dilations[i] - 1)
              h = paddle.eye(new_height, dtype=x.dtype)[
                  :: x_dilations[i]
              ]
              x = paddle_backend.swapaxes(x, 1 + i, -1)
              x = paddle.matmul(x, h)
              x = paddle_backend.swapaxes(x, -1, 1 + i)
  
      df = (
          "NLC"
          if dims == 1
          else _get_x_data_format(dims, data_format="channel_last")
      )
      x = _pad_before_conv(
          x, filters, strides, padding, dims, dilations, df
      )
      filters = paddle.transpose(
          filters, perm=(dims + 1, dims, *range(dims))
      )
      padding = "VALID"
  
      if dims == 1:
          res = paddle.nn.functional.conv1d(
              x,
              filters,
              bias=bias,
              data_format=df,
              stride=strides,
              padding=padding,
              dilation=dilations,
              groups=feature_group_count,
          )
      elif dims == 2:
          res = paddle.nn.functional.conv2d(
              x,
              filters,
              bias=bias,
              data_format=df,
              stride=strides,
              padding=padding,
              dilation=dilations,
              groups=feature_group_count,
          )
      elif dims == 3:
          res = paddle.nn.functional.conv3d(
              x,
              filters,
              bias=bias,
              data_format=df,
              stride=strides,
              padding=padding,
              dilation=dilations,
              groups=feature_group_count,
          )
  
      if data_format == "channel_first":
          res = paddle.transpose(
              res, perm=(0, dims + 1, *range(1, dims + 1))
          )
      return res
  
imports: from typing import Optional, Tuple, Union, Sequence
  import paddle
  import ivy
  from ivy.func_wrapper import with_unsupported_device_and_dtypes
  from ivy.utils.exceptions import IvyNotImplementedException
  from ivy.functional.ivy.layers import _handle_padding, _get_x_data_format, _deconv_length
  import ivy.functional.backends.paddle
  from .None import backend_version
label_desc: Use image node_id 13 for calling a global function with example usage: conv_general_dilated() and returns: res