docstr: ivy.Container static method variant of ivy.quantile. This method
  simply wraps the function, and so the docstring for ivy.quantile also
  applies to this method with minimal changes.
  
  Parameters
  ----------
  a
      Input container including arrays.
  q
      Quantile or sequence of quantiles to compute, which must be
      between 0 and 1 inclusive.
  axis
      Axis or axes along which the quantiles are computed. The default
      is to compute the quantile(s) along a flattened version of the array.
  keepdims
      If this is set to True, the axes which are reduced are left in the result
      as dimensions with size one. With this option, the result will broadcast
      correctly against the original array a.
  interpolation
      {'nearest', 'linear', 'lower', 'higher', 'midpoint'}. Default value:
      'linear'.
      This specifies the interpolation method to use when the desired quantile
      lies between two data points i < j:
      - linear: i + (j - i) * fraction, where fraction is the fractional part of
      the index surrounded by i and j.
      - lower: i.
      - higher: j.
      - nearest: i or j, whichever is nearest.
      - midpoint: (i + j) / 2. linear and midpoint interpolation do not work with
      integer dtypes.
  out
      optional output array, for writing the result to.
  
  Returns
  -------
  ret
      Container with (rank(q) + N - len(axis)) dimensional arrays of same dtype
      as input arrays in the container, or, if axis is None, rank(q) arrays. The
      first rank(q) dimensions index quantiles for different values of q.
  
  Examples
  --------
  With one :class:`ivy.Container` input:
  
  >>> a = ivy.Container(x=ivy.array([[10., 7., 4.], [3., 2., 1.]]),
                        y=ivy.array([1., 2., 3., 4.]))
  >>> q = 0.5
  >>> b = ivy.Container.static_quantile(a, q)
  >>> print(b)
  {
      x: 3.5,
      y: 2.5
  }
  
  >>> a = ivy.Container(x=ivy.array([[10., 7., 4.], [3., 2., 1.]]),
                        y=ivy.array([1., 2., 3., 4.]))
  >>> q = ivy.array([0.5, 0.75])
  >>> b = ivy.Container.static_quantile(a, q)
  >>> print(b)
  {
      x: ivy.array([3.5, 6.25]),
      y: ivy.array([2.5, 3.25])
  }
  
  >>> a = ivy.Container(x=ivy.array([[10., 7., 4.], [3., 2., 1.]]),
                        y=ivy.array([1., 2., 3., 4.]))
  >>> q = ivy.array([0.5, 0.75])
  >>> b = ivy.Container.static_quantile(a, q, axis = 0)
  >>> print(b)
  {
      x: ivy.array([[6.5, 4.5, 2.5],
                  [8.25, 5.75, 3.25]]),
      y: ivy.array([2.5, 3.25])
  }
  
  >>> a = ivy.Container(x=ivy.array([[10., 7., 4.], [3., 2., 1.]]))
  >>> b = ivy.Container.static_quantile(a, q, axis = 1, keepdims=True)
  >>> print(b)
  {
      x: ivy.array([[[7.],
              [2.]],
              [[8.5],
              [2.5]]])
  }
  
  >>> a = ivy.Container(x=ivy.array([[10., 7., 4.], [3., 2., 1.]]),
                        y=ivy.array([1., 2., 3., 4.]))
  >>> q = ivy.array([0.3, 0.7])
  >>> b = ivy.Container.static_quantile(a, q, axis = 0, interpolation="lower")
  >>> print(b)
  {
      x: ivy.array([[3., 2., 1.],
                  [3., 2., 1.]]),
      y: ivy.array([1., 3.])
  }
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code: def static_quantile(
      a: Union[ivy.Container, ivy.Array, ivy.NativeArray],
      q: Union[ivy.Array, float, ivy.Container],
      /,
      *,
      axis: Optional[Union[Sequence[int], int, ivy.Container]] = None,
      keepdims: Union[bool, ivy.Container] = False,
      interpolation: Union[str, ivy.Container] = "linear",
      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.quantile. This method
      simply wraps the function, and so the docstring for ivy.quantile also
      applies to this method with minimal changes.
  
      Parameters
      ----------
      a
          Input container including arrays.
      q
          Quantile or sequence of quantiles to compute, which must be
          between 0 and 1 inclusive.
      axis
          Axis or axes along which the quantiles are computed. The default
          is to compute the quantile(s) along a flattened version of the array.
      keepdims
          If this is set to True, the axes which are reduced are left in the result
          as dimensions with size one. With this option, the result will broadcast
          correctly against the original array a.
      interpolation
          {'nearest', 'linear', 'lower', 'higher', 'midpoint'}. Default value:
          'linear'.
          This specifies the interpolation method to use when the desired quantile
          lies between two data points i < j:
          - linear: i + (j - i) * fraction, where fraction is the fractional part of
          the index surrounded by i and j.
          - lower: i.
          - higher: j.
          - nearest: i or j, whichever is nearest.
          - midpoint: (i + j) / 2. linear and midpoint interpolation do not work with
          integer dtypes.
      out
          optional output array, for writing the result to.
  
      Returns
      -------
      ret
          Container with (rank(q) + N - len(axis)) dimensional arrays of same dtype
          as input arrays in the container, or, if axis is None, rank(q) arrays. The
          first rank(q) dimensions index quantiles for different values of q.
  
      Examples
      --------
      With one :class:`ivy.Container` input:
  
      >>> a = ivy.Container(x=ivy.array([[10., 7., 4.], [3., 2., 1.]]),
                            y=ivy.array([1., 2., 3., 4.]))
      >>> q = 0.5
      >>> b = ivy.Container.static_quantile(a, q)
      >>> print(b)
      {
          x: 3.5,
          y: 2.5
      }
  
      >>> a = ivy.Container(x=ivy.array([[10., 7., 4.], [3., 2., 1.]]),
                            y=ivy.array([1., 2., 3., 4.]))
      >>> q = ivy.array([0.5, 0.75])
      >>> b = ivy.Container.static_quantile(a, q)
      >>> print(b)
      {
          x: ivy.array([3.5, 6.25]),
          y: ivy.array([2.5, 3.25])
      }
  
      >>> a = ivy.Container(x=ivy.array([[10., 7., 4.], [3., 2., 1.]]),
                            y=ivy.array([1., 2., 3., 4.]))
      >>> q = ivy.array([0.5, 0.75])
      >>> b = ivy.Container.static_quantile(a, q, axis = 0)
      >>> print(b)
      {
          x: ivy.array([[6.5, 4.5, 2.5],
                      [8.25, 5.75, 3.25]]),
          y: ivy.array([2.5, 3.25])
      }
  
      >>> a = ivy.Container(x=ivy.array([[10., 7., 4.], [3., 2., 1.]]))
      >>> b = ivy.Container.static_quantile(a, q, axis = 1, keepdims=True)
      >>> print(b)
      {
          x: ivy.array([[[7.],
                  [2.]],
                  [[8.5],
                  [2.5]]])
      }
  
      >>> a = ivy.Container(x=ivy.array([[10., 7., 4.], [3., 2., 1.]]),
                            y=ivy.array([1., 2., 3., 4.]))
      >>> q = ivy.array([0.3, 0.7])
      >>> b = ivy.Container.static_quantile(a, q, axis = 0, interpolation="lower")
      >>> print(b)
      {
          x: ivy.array([[3., 2., 1.],
                      [3., 2., 1.]]),
          y: ivy.array([1., 3.])
      }
      """
      return ContainerBase.cont_multi_map_in_function(
          "quantile",
          a,
          q,
          axis=axis,
          keepdims=keepdims,
          interpolation=interpolation,
          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, Sequence
  import ivy
  from ivy.data_classes.container.base import ContainerBase
label_desc: Use image node_id 11 for calling the _ContainerWithStatisticalExperimental obj's underlying member method code with example usage: obj.static_quantile() and returns: ContainerBase