code: def conv(
      x: Union[ivy.Array, ivy.NativeArray],
      filters: Union[ivy.Array, ivy.NativeArray],
      strides: Union[
          int, Tuple[int], Tuple[int, int], Tuple[int, int, int]
      ],
      padding: Union[str, Sequence[Tuple[int, int]]],
      /,
      *,
      transpose: bool = False,
      dims: int = 2,
      output_shape: Optional[Union[ivy.Shape, ivy.NativeShape]] = None,
      data_format: str = "channel_last",
      filter_format: str = "channel_last",
      feature_group_count: int = 1,
      x_dilations: Union[
          int, Tuple[int], Tuple[int, int], Tuple[int, int, int]
      ] = 1,
      dilations: Union[
          int, Tuple[int], Tuple[int, int], Tuple[int, int, int]
      ] = 1,
      bias: Optional[Union[ivy.Array, ivy.NativeArray]] = None,
      out: Optional[ivy.Array] = None,
  ) -> ivy.Array:
      """Compute a 1-D, 2-D, and 3-D transpose or dilated convolution given 3-D,
      4-D and 5-D input x respectively and filters arrays.
  
      Parameters
      ----------
      x
          Input image *[batch_size,d,h,w,d_in]* or *[batch_size,d_in,d,h,w]*.
      filters
          Convolution filters *[fd,fh,fw,d_in/feature_group_count,d_out]*.
      strides
          The stride of the sliding window for each dimension of input.
      padding
          either the string ‘SAME’ (padding with zeros evenly), the string ‘VALID’ (no
          padding), or a sequence of n (low, high) integer pairs that give the padding to
          apply before and after each spatial dimension.
      transpose
          True for computing transpose convolution, and False for dilated convolution.
          When True, `x_dilations` must be 1 (the default).
      dims
          Either 1, 2, or 3 corresponding to 1-D, 2-D, and 3-D convolution.
      output_shape
          Shape of the output (Default value = None)
      data_format
          Either "channel_first" or "channel_last". "channel_first" corresponds to "NCW",
          "NCHW", "NCDHW" input data formatS for 1-D, 2-D, 3-D convolution respectively,
          while "channel_last" corresponds to "NWC", "NHWC", "NDHWC" respectively.
      filter_format
          Either "channel_first" or "channel_last". "channel_first" corresponds to "OIW",
          "OIHW", "OIDHW" input data formats for 1-D, 2-D, 3-D convolution respectively,
          while "channel_last" corresponds to "WIO", "HWIO", "DHWIO" respectively.
      feature_group_count
           split input into groups, d_in should be divisible by the number of groups.
           (Default value = 1)
      x_dilations
          The dilation factor for each dimension of input. (Default value = 1)
      dilations
          The dilation factor for each dimension of input. (Default value = 1)
      bias
          Bias array of shape *[d_out]*.
      out
          optional output array, for writing the result to. It must have a shape that the
          inputs broadcast to.
  
      Returns
      -------
      ret
          The result of the transpose or dilated convolution operation.
      """
      if transpose:
          return conv_general_transpose(
              x,
              filters,
              strides,
              padding,
              dims=dims,
              output_shape=output_shape,
              data_format=data_format,
              dilations=dilations,
              feature_group_count=feature_group_count,
              bias=bias,
              out=out,
          )
      else:
          return conv_general_dilated(
              x,
              filters,
              strides,
              padding,
              dims=dims,
              data_format=data_format,
              filter_format=filter_format,
              feature_group_count=feature_group_count,
              x_dilations=x_dilations,
              dilations=dilations,
              bias=bias,
              out=out,
          )
  
imports: from typing import Optional, Tuple, Union, Sequence
  import ivy
  from ivy.utils.backend import current_backend
  from ivy.func_wrapper import handle_array_function, handle_partial_mixed_function, inputs_to_ivy_arrays, to_native_arrays_and_back, inputs_to_native_shapes, handle_out_argument, handle_nestable, handle_array_like_without_promotion, handle_device, handle_backend_invalid
  from ivy.utils.exceptions import handle_exceptions
label_desc: Use image node_id 16 for calling a global function with example usage: conv() and returns: conv_general_transpose, conv_general_dilated