docstr: ivy.Container static method variant of ivy.dct. This method simply
  wraps the function, and so the docstring for ivy.dct also applies to
  this method with minimal changes.
  
  Parameters
  ----------
  x
      Container with the input signals.
  type
      The type of the dct. Must be 1, 2, 3 or 4.
  n
      The length of the transform. If n is less than the input signal length,
      then x is truncated, if n is larger than x is zero-padded.
  norm
      The type of normalization to be applied. Must be either None or "ortho".
  out
      optional output container, for writing the result to.
  
  Returns
  -------
  ret
      The transformed input.
  
  Examples
  --------
  With one :class:`ivy.Container` input:
  
  >>> x = ivy.Container(a=ivy.array([8, 16, 24, 32, 40, 48, 56, 64]),
  ...                   b=ivy.array([1,  2,  3,  4,  5,  6,  7,  8]))
  >>> ivy.Container.static_dct(x, type=2, norm='ortho')
  {
      a: ivy.array([102., -51.5, 0., -5.39, 0., -1.61, 0.,
                  -0.406]),
      b: ivy.array([12.7, -6.44, 0., -0.673, 0., -0.201, 0.,
                  -0.0507])
  }
  
  With multiple :class:`ivy.Container` inputs:
  
  >>> x = ivy.Container(a=ivy.array([  8, 16,  24,  32,   40,   48,   56,   64]),
  ...                   b=ivy.array([11., 54, 23., 13., 255., 255., 132., 182.]))
  >>> n = ivy.Container(a=9, b=5)
  >>> type = ivy.Container(a=2, b=4)
  >>> norm = ivy.Container(a="ortho", b=None)
  >>> ivy.Container.static_dct(x, type=type, n=n, norm=norm)
  {
      a: ivy.array([96., -28.2, -31.9, 22.9, -26., 19.8, -17., 10.9,
                  -5.89]),
      b: ivy.array([242., -253., 286., -515., 467.])
  }
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code: def static_dct(
      x: ivy.Container,
      /,
      *,
      type: Union[Literal[1, 2, 3, 4], ivy.Container] = 2,
      n: Optional[Union[int, ivy.Container]] = None,
      axis: Union[int, ivy.Container] = -1,
      norm: Optional[Union[Literal["ortho"], ivy.Container]] = None,
      key_chains: Optional[
          Union[List[str], Dict[str, str], ivy.Container]
      ] = None,
      to_apply: Union[bool, ivy.Container] = True,
      prune_unapplied: Union[bool, ivy.Container] = False,
      map_sequences: Union[bool, ivy.Container] = False,
      out: Optional[ivy.Container] = None,
  ) -> ivy.Container:
      """ivy.Container static method variant of ivy.dct. This method simply
      wraps the function, and so the docstring for ivy.dct also applies to
      this method with minimal changes.
  
      Parameters
      ----------
      x
          Container with the input signals.
      type
          The type of the dct. Must be 1, 2, 3 or 4.
      n
          The length of the transform. If n is less than the input signal length,
          then x is truncated, if n is larger than x is zero-padded.
      norm
          The type of normalization to be applied. Must be either None or "ortho".
      out
          optional output container, for writing the result to.
  
      Returns
      -------
      ret
          The transformed input.
  
      Examples
      --------
      With one :class:`ivy.Container` input:
  
      >>> x = ivy.Container(a=ivy.array([8, 16, 24, 32, 40, 48, 56, 64]),
      ...                   b=ivy.array([1,  2,  3,  4,  5,  6,  7,  8]))
      >>> ivy.Container.static_dct(x, type=2, norm='ortho')
      {
          a: ivy.array([102., -51.5, 0., -5.39, 0., -1.61, 0.,
                      -0.406]),
          b: ivy.array([12.7, -6.44, 0., -0.673, 0., -0.201, 0.,
                      -0.0507])
      }
  
      With multiple :class:`ivy.Container` inputs:
  
      >>> x = ivy.Container(a=ivy.array([  8, 16,  24,  32,   40,   48,   56,   64]),
      ...                   b=ivy.array([11., 54, 23., 13., 255., 255., 132., 182.]))
      >>> n = ivy.Container(a=9, b=5)
      >>> type = ivy.Container(a=2, b=4)
      >>> norm = ivy.Container(a="ortho", b=None)
      >>> ivy.Container.static_dct(x, type=type, n=n, norm=norm)
      {
          a: ivy.array([96., -28.2, -31.9, 22.9, -26., 19.8, -17., 10.9,
                      -5.89]),
          b: ivy.array([242., -253., 286., -515., 467.])
      }
      """
      return ContainerBase.cont_multi_map_in_function(
          "dct",
          x,
          type=type,
          n=n,
          axis=axis,
          norm=norm,
          key_chains=key_chains,
          to_apply=to_apply,
          prune_unapplied=prune_unapplied,
          map_sequences=map_sequences,
          out=out,
      )
  
imports: from typing import Optional, Union, List, Dict, Tuple, Literal, Sequence, Callable
  import ivy
  from ivy.data_classes.container.base import ContainerBase
label_desc: Use image node_id 13 for calling the _ContainerWithLayersExperimental obj's underlying member method code with example usage: obj.static_dct() and returns: ContainerBase