docstr: Parameters
  ----------
  axis
  inverse
  onesided
  dft_length
  norm
  key_chains
  to_apply
  prune_unapplied
  map_sequences
  out
docstr_tok: ["Parameters","--","--","--","--","--","axis","inverse","onesided","dft_length","norm","key_chains","to_apply","prune_unapplied","map_sequences","out"]
code: def dft(
      self: Union[ivy.Array, ivy.NativeArray, ivy.Container],
      /,
      *,
      axis: Union[int, ivy.Container] = 1,
      inverse: Union[bool, ivy.Container] = False,
      onesided: Union[bool, ivy.Container] = False,
      dft_length: Optional[
          Union[int, Tuple[int], ivy.Container]
      ] = None,
      norm: Union[str, ivy.Container] = "backward",
      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[Union[ivy.Array, ivy.Container]] = None,
  ) -> ivy.Container:
      """
  
      Parameters
      ----------
      axis
      inverse
      onesided
      dft_length
      norm
      key_chains
      to_apply
      prune_unapplied
      map_sequences
      out
  
      """
      return self.static_dft(
          self,
          axis=axis,
          inverse=inverse,
          onesided=onesided,
          dft_length=dft_length,
          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 24 for calling the _ContainerWithLayersExperimental obj's underlying member method code with example usage: obj.dft() and returns: self