docstr: ivy.Container static method variant of ivy.fft. This method simply
  wraps the function, and so the docstring for ivy.fft also applies to
  this method with minimal changes.
  
  Parameters
  ----------
  x
      Container containing input volumes *[...,d_in,...]*,
      where d_in indicates the dimension that needs FFT.
  dim
      The dimension along which to take the one dimensional FFT.
  norm
      Optional argument, "backward", "ortho" or "forward". Defaults to be
      "backward".
      "backward" indicates no normalization.
      "ortho" indicates normalization by 1/sqrt(n).
      "forward" indicates normalization by 1/n.
  n
      Optional argument indicating the sequence length, if given, the input
      would be padded with zero or truncated to length n before performing FFT.
      Should be a integer greater than 1.
  out
      Optional output array, for writing the result to. It must have a shape that
      the inputs broadcast to.
  
  Returns
  -------
  ret
      The transformed input.
  
  Examples
  --------
  >>> a = ivy.array(np.array([ 6.+0.j, -2.+2.j, -2.+0.j, -2.-2.j]))
  >>> b = ivy.array(np.exp(2j * np.pi * np.arange(8) / 8))
  >>> c = ivy.Container(a=a, b=b)
  >>> dims = ivy.Container(a=0, b=0)
  >>> ivy.Container.static_fft(c, dims)
  {
  a: ivy.array([0.+0.j, 12.+0.j, 8.+0.j, 4.+0.j]),
  b: ivy.array([-3.44509285e-16+1.14423775e-17j, 8.00000000e+00-8.11483250e-16j,
                 2.33486982e-16+1.22464680e-16j, 0.00000000e+00+1.22464680e-16j,
                 9.95799250e-17+2.33486982e-16j, 0.00000000e+00+7.66951701e-17j,
                 1.14423775e-17+1.22464680e-16j, 0.00000000e+00+1.22464680e-16j])
  }
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code: def _static_fft(
      x: Union[ivy.Container, ivy.Array, ivy.NativeArray],
      dim: Union[int, ivy.Container],
      /,
      *,
      norm: Union[str, ivy.Container] = "backward",
      n: Optional[Union[int, Tuple[int], ivy.Container]] = None,
      out: Optional[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,
  ) -> ivy.Container:
      """ivy.Container static method variant of ivy.fft. This method simply
      wraps the function, and so the docstring for ivy.fft also applies to
      this method with minimal changes.
  
      Parameters
      ----------
      x
          Container containing input volumes *[...,d_in,...]*,
          where d_in indicates the dimension that needs FFT.
      dim
          The dimension along which to take the one dimensional FFT.
      norm
          Optional argument, "backward", "ortho" or "forward". Defaults to be
          "backward".
          "backward" indicates no normalization.
          "ortho" indicates normalization by 1/sqrt(n).
          "forward" indicates normalization by 1/n.
      n
          Optional argument indicating the sequence length, if given, the input
          would be padded with zero or truncated to length n before performing FFT.
          Should be a integer greater than 1.
      out
          Optional output array, for writing the result to. It must have a shape that
          the inputs broadcast to.
  
      Returns
      -------
      ret
          The transformed input.
  
      Examples
      --------
      >>> a = ivy.array(np.array([ 6.+0.j, -2.+2.j, -2.+0.j, -2.-2.j]))
      >>> b = ivy.array(np.exp(2j * np.pi * np.arange(8) / 8))
      >>> c = ivy.Container(a=a, b=b)
      >>> dims = ivy.Container(a=0, b=0)
      >>> ivy.Container.static_fft(c, dims)
      {
      a: ivy.array([0.+0.j, 12.+0.j, 8.+0.j, 4.+0.j]),
      b: ivy.array([-3.44509285e-16+1.14423775e-17j, 8.00000000e+00-8.11483250e-16j,
                     2.33486982e-16+1.22464680e-16j, 0.00000000e+00+1.22464680e-16j,
                     9.95799250e-17+2.33486982e-16j, 0.00000000e+00+7.66951701e-17j,
                     1.14423775e-17+1.22464680e-16j, 0.00000000e+00+1.22464680e-16j])
      }
      """
      return ContainerBase.cont_multi_map_in_function(
          "fft",
          x,
          dim,
          norm=norm,
          n=n,
          out=out,
          key_chains=key_chains,
          to_apply=to_apply,
          prune_unapplied=prune_unapplied,
          map_sequences=map_sequences,
      )
  
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 17 for calling the _ContainerWithLayersExperimental obj's underlying member method code with example usage: obj._static_fft() and returns: ContainerBase