docstr: ivy.Container static method variant of ivy.rfft.
  
  This method simply wraps the function, and so the docstring for
  ivy.rfft also applies to this method with minimal changes.
  
  Parameters
  ----------
  x
      input array. Must have a real-valued floating-point data type.
  n
      length of the transformed axis of the input. If
      -   n is greater than the length of the input array, the input array
      is zero-padded to length n.
      -   n is less than the length of the input array, the input array is
      trimmed to length n.
      -   n is not provided, the length of the transformed axis of the
      output must equal the length of the input along the axis specified
      by axis. Default is ``None``.
  axis
      axis (dimension) over which to compute the Fourier transform.
      If not set, the last axis (dimension) is used. Default is ``-1``.
  norm
      normalization mode. Should be one of the following modes:
      -   'backward': no normalization.
      -   'ortho': normalize by 1/sqrt(n) (i.e., make the FFT orthonormal).
      -   'forward': normalize by 1/n.
      Default is ``backward``.
  out
      Optional output array, for writing the result to. It must
      have a shape that the inputs broadcast to.
  key_chains
      The key-chains to apply or not apply the method to.
      Default is ``None``.
  to_apply
      If True, the method will be applied to key_chains,
      otherwise key_chains will be skipped. Default is ``True``.
  prune_unapplied
      Whether to prune key_chains for which the function was
      not applied. Default is ``False``.
  map_sequences
      Whether to also map method to sequences (lists, tuples).
      Default is ``False``.
  
  Returns
  -------
  ret
      an array transformed along the axis (dimension) indicated by axis.
      The returned array must have a complex-valued floating-point
      data type determined by Type Promotion Rules.
  
  Examples
  --------
  >>> x = ivy.Container(a=ivy.array([0.,1.,2.]),
  ...                   b=ivy.array([3.,4.,5.]))
  >>> y =  ivy.Container.static_rfft(x)
  >>> print(y)
  {
      a: ivy.array([3.+0.j, -1.5+0.8660254j]),
      b: ivy.array([12.+0.j, -1.5+0.8660254j])
  }
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code: def static_rfft(
      x: ivy.Container,
      /,
      *,
      n: Optional[Union[int, ivy.Container]] = None,
      axis: Union[int, ivy.Container] = -1,
      norm: Union[
          Literal["backward", "ortho", "forward"], ivy.Container
      ] = "backward",
      out: Optional[Union[ivy.Array, 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.rfft.
  
      This method simply wraps the function, and so the docstring for
      ivy.rfft also applies to this method with minimal changes.
  
      Parameters
      ----------
      x
          input array. Must have a real-valued floating-point data type.
      n
          length of the transformed axis of the input. If
          -   n is greater than the length of the input array, the input array
          is zero-padded to length n.
          -   n is less than the length of the input array, the input array is
          trimmed to length n.
          -   n is not provided, the length of the transformed axis of the
          output must equal the length of the input along the axis specified
          by axis. Default is ``None``.
      axis
          axis (dimension) over which to compute the Fourier transform.
          If not set, the last axis (dimension) is used. Default is ``-1``.
      norm
          normalization mode. Should be one of the following modes:
          -   'backward': no normalization.
          -   'ortho': normalize by 1/sqrt(n) (i.e., make the FFT orthonormal).
          -   'forward': normalize by 1/n.
          Default is ``backward``.
      out
          Optional output array, for writing the result to. It must
          have a shape that the inputs broadcast to.
      key_chains
          The key-chains to apply or not apply the method to.
          Default is ``None``.
      to_apply
          If True, the method will be applied to key_chains,
          otherwise key_chains will be skipped. Default is ``True``.
      prune_unapplied
          Whether to prune key_chains for which the function was
          not applied. Default is ``False``.
      map_sequences
          Whether to also map method to sequences (lists, tuples).
          Default is ``False``.
  
      Returns
      -------
      ret
          an array transformed along the axis (dimension) indicated by axis.
          The returned array must have a complex-valued floating-point
          data type determined by Type Promotion Rules.
  
      Examples
      --------
      >>> x = ivy.Container(a=ivy.array([0.,1.,2.]),
      ...                   b=ivy.array([3.,4.,5.]))
      >>> y =  ivy.Container.static_rfft(x)
      >>> print(y)
      {
          a: ivy.array([3.+0.j, -1.5+0.8660254j]),
          b: ivy.array([12.+0.j, -1.5+0.8660254j])
      }
      """
      return ContainerBase.cont_multi_map_in_function(
          "rfft",
          x,
          n=n,
          axis=axis,
          norm=norm,
          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 35 for calling the _ContainerWithLayersExperimental obj's underlying member method code with example usage: obj.static_rfft() and returns: ContainerBase