code: def dct(
      x: JaxArray,
      /,
      *,
      type: Literal[1, 2, 3, 4] = 2,
      n: Optional[int] = None,
      axis: int = -1,
      norm: Optional[Literal["ortho"]] = None,
      out: Optional[JaxArray] = None,
  ) -> JaxArray:
      if norm not in (None, "ortho"):
          raise ValueError("Norm must be either None or 'ortho'")
      if axis < 0:
          axis += len(x.shape)
      if n is not None:
          signal_len = x.shape[axis]
          if n <= signal_len:
              local_idx = [slice(None)] * len(x.shape)
              local_idx[axis] = slice(None, n)
              x = x[tuple(local_idx)]
          else:
              pad_idx = [[0, 0] for _ in range(len(x.shape))]
              pad_idx[axis][1] = n - signal_len
              x = jnp.pad(x, pad_idx)
      real_zero = jnp.array(0.0, dtype=x.dtype)
      axis_dim = x.shape[axis]
      axis_dim_float = jnp.array(axis_dim, dtype=x.dtype)
  
      if type == 1:
          if norm:
              raise ValueError(
                  "Normalization not supported for type-I DCT"
              )
          axis_idx = [slice(None)] * len(x.shape)
          axis_idx[axis] = slice(-2, 0, -1)
          x = jnp.concatenate([x, x[tuple(axis_idx)]], axis=axis)
          dct_out = jnp.real(jnp.fft.rfft(x, axis=axis))
          return dct_out
  
      elif type == 2:
          dct_out = jax.scipy.fft.dct(
              x, type=2, n=n, axis=axis, norm=norm
          )
          return dct_out
  
      elif type == 3:
          scale_dims = [1] * len(x.shape)
          scale_dims[axis] = axis_dim
          scale = 2.0 * jnp.exp(
              jlax.complex(
                  real_zero,
                  jnp.arange(axis_dim_float)
                  * math.pi
                  * 0.5
                  / axis_dim_float,
              )
          ).reshape(scale_dims)
          if norm == "ortho":
              n1 = jnp.sqrt(axis_dim_float)
              n2 = n1 * jnp.sqrt(0.5)
              sf = jnp.pad(
                  jnp.expand_dims(n1, 0),
                  (0, axis_dim - 1),
                  constant_values=n2,
              )
              x = x * sf.reshape(scale_dims)
          else:
              x = x * axis_dim_float
  
          axis_idx = [slice(None)] * len(x.shape)
          axis_idx[axis] = slice(None, axis_dim)
          dct_out = jnp.real(
              jnp.fft.irfft(
                  scale * jlax.complex(x, real_zero),
                  n=2 * axis_dim,
                  axis=axis,
              )
          )[tuple(axis_idx)]
          return dct_out
  
      elif type == 4:
          dct_2 = jax.scipy.fft.dct(
              x, type=2, n=2 * axis_dim, axis=axis, norm=None
          )
          axis_idx = [slice(None)] * len(x.shape)
          axis_idx[axis] = slice(1, None, 2)
          dct_out = dct_2[tuple(axis_idx)]
          if norm == "ortho":
              dct_out *= math.sqrt(0.5) * jlax.rsqrt(axis_dim_float)
      return dct_out
  
imports: from typing import Optional, Union, Tuple, List, Literal, Sequence, Callable
  import jax
  import jax.lax
  import jax.numpy
  import math
  import ivy
  from ivy import output_to_native_arrays
  from ivy.functional.backends.jax import JaxArray
  from ivy.functional.backends.jax.random import RNG
  from ivy.functional.ivy.experimental.general import _correct_ivy_callable
  from ivy.functional.ivy.layers import _handle_padding, _validate_max_pool_params, _depth_max_pooling_helper
  from ivy.functional.ivy.experimental.layers import _padding_ceil_mode, _get_size
  from ivy.func_wrapper import with_supported_dtypes
  from ivy.func_wrapper import with_unsupported_dtypes
  from .None import backend_version
  from ivy.functional.backends.jax.experimental.manipulation import _to_nested_tuple
label_desc: Use image node_id 10 for calling a global function with example usage: dct() and returns: dct_out, dct_out, dct_out, dct_out