docstr: ivy.Container instance method variant of ivy.ifft. This method
  simply wraps the function, and so the docstring for ivy.ifft also
  applies to this method with minimal changes.
  
  Parameters
  ----------
  self
      Container containing input volumes *[...,d_in,...]*,
      where d_in indicates the dimension that needs IFFT.
  dim
      The dimension along which to take the one dimensional IFFT.
  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 IFFT.
      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
      Container containing the transformed inputs.
  
  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)
  >>> c.ifft(dims)
  {
  a: ivy.array([0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j]),
  b: ivy.array([-4.30636606e-17+1.43029718e-18j, 0.00000000e+00+1.53080850e-17j,
                 1.43029718e-18+1.53080850e-17j, 0.00000000e+00+9.58689626e-18j,
                 1.24474906e-17+2.91858728e-17j, 0.00000000e+00+1.53080850e-17j,
                 2.91858728e-17+1.53080850e-17j, 1.00000000e+00-1.01435406e-16j])
  }
docstr_tok: ["ivy.Container","instance","method","variant","of","ivy.ifft",".","This","method","simply","wraps","the","function",",","and","so","the","docstring","for","ivy.ifft","also","applies","to","this","method","with","minimal","changes",".","Parameters","--","--","--","--","--","self","Container","containing","input","volumes","*","[","...",",","d_in",",","...","]","*",",","where","d_in","indicates","the","dimension","that","needs","IFFT",".","dim","The","dimension","along","which","to","take","the","one","dimensional","IFFT",".","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","IFFT",".","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","Container","containing","the","transformed","inputs",".","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",")",">",">",">","c.ifft","(","dims",")","{","a",":","ivy.array","(","[","0.+0.j",",","1.+0.j",",","2.+0.j",",","3.+0.j","]",")",",","b",":","ivy.array","(","[","-4.30636606e-17+1.43029718e-18j",",","0.00000000e+00+1.53080850e-17j",",","1.43029718e-18+1.53080850e-17j",",","0.00000000e+00+9.58689626e-18j",",","1.24474906e-17+2.91858728e-17j",",","0.00000000e+00+1.53080850e-17j",",","2.91858728e-17+1.53080850e-17j",",","1.00000000e+00-1.01435406e-16j","]",")","}"]
code: def ifft(
      self: ivy.Container,
      dim: Union[int, ivy.Container],
      *,
      norm: Union[str, ivy.Container] = "backward",
      n: Optional[Union[int, Tuple[int], ivy.Container]] = None,
      out: Optional[Union[ivy.Array, ivy.Container]] = None,
  ):
      """ivy.Container instance method variant of ivy.ifft. This method
      simply wraps the function, and so the docstring for ivy.ifft also
      applies to this method with minimal changes.
  
      Parameters
      ----------
      self
          Container containing input volumes *[...,d_in,...]*,
          where d_in indicates the dimension that needs IFFT.
      dim
          The dimension along which to take the one dimensional IFFT.
      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 IFFT.
          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
          Container containing the transformed inputs.
  
      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)
      >>> c.ifft(dims)
      {
      a: ivy.array([0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j]),
      b: ivy.array([-4.30636606e-17+1.43029718e-18j, 0.00000000e+00+1.53080850e-17j,
                     1.43029718e-18+1.53080850e-17j, 0.00000000e+00+9.58689626e-18j,
                     1.24474906e-17+2.91858728e-17j, 0.00000000e+00+1.53080850e-17j,
                     2.91858728e-17+1.53080850e-17j, 1.00000000e+00-1.01435406e-16j])
      }
      """
      return self.static_ifft(
          self,
          dim,
          norm=norm,
          n=n,
          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 20 for calling the _ContainerWithLayersExperimental obj's underlying member method code with example usage: obj.ifft(dim) and returns: self