code: def idct(
      x: torch.Tensor,
      /,
      *,
      type: Literal[1, 2, 3, 4] = 2,
      n: Optional[int] = None,
      axis: int = -1,
      norm: Optional[Literal["ortho"]] = None,
      out: Optional[torch.Tensor] = None,
  ) -> torch.tensor:
      inverse_type = {1: 1, 2: 3, 3: 2, 4: 4}[type]
      return dct(
          x, type=inverse_type, n=n, axis=axis, norm=norm, out=out
      )
  
imports: from typing import Optional, Union, Tuple, List, Literal, Sequence, Callable
  import torch
  import math
  import ivy
  from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
  from .None import backend_version
  from ivy.functional.ivy.layers import _handle_padding, _get_num_padded_values, _validate_max_pool_params, _depth_max_pooling_helper
  from ivy.functional.ivy.experimental.layers import _padding_ceil_mode, _broadcast_pooling_helper
label_desc: Use image node_id 12 for calling a global function with example usage: idct() and returns: dct