docstr: ivy.Container instance method variant of ivy.nanmedian. This method
  simply wraps the function, and so the docstring for ivy.nanmedian also
  applies to this method with minimal changes.
  
  Parameters
  ----------
  self
      Input array.
  axis
      The axis or axes along which the means are computed.
      The default is to compute the mean of the flattened 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 container. If the value is anything
      but the default, then keepdims will be passed through to the mean or
      sum methods of sub-classes of ndarray. If the sub-classes methods
      does not implement keepdims any exceptions will be raised.
  overwrite_input
      If True, then allow use of memory of input array a for calculations.
      The input array will be modified by the call to median.
      This will save memory when you do not need to preserve
      the contents of the input array.Treat the input as undefined,
      but it will probably be fully or partially sorted.
      Default is False. If overwrite_input is True and
      input container does not already have leaves which are
      of the ndarray kind, an error will be raised.
  out
      optional output array, for writing the result to.
  
  Returns
  -------
  ret
      A new array holding the result. If the input contains integers
  
  Examples
  --------
  With :class:`ivy.Container` input and default backend set as `numpy`:
  >>> x = ivy.Container(a=ivy.array([[10.0, ivy.nan, 4], [3, 2, 1]]),
          b=ivy.array([[12, 10, 34], [45, 23, ivy.nan]]))
  >>> x.nanmedian()
  {
      a: ivy.array(3.),
      b: ivy.array(23.)
  }
  >>> x.nanmedian(axis=0)
  {
      a: ivy.array([6.5, 2., 2.5]),
      b: ivy.array([28.5, 16.5, 34.])
  }
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code: def nanmedian(
      self: ivy.Container,
      /,
      *,
      axis: Optional[Union[Tuple[int], int, ivy.Container]] = None,
      keepdims: Union[bool, ivy.Container] = False,
      overwrite_input: Union[bool, ivy.Container] = False,
      out: Optional[ivy.Container] = None,
  ) -> ivy.Container:
      """ivy.Container instance method variant of ivy.nanmedian. This method
      simply wraps the function, and so the docstring for ivy.nanmedian also
      applies to this method with minimal changes.
  
      Parameters
      ----------
      self
          Input array.
      axis
          The axis or axes along which the means are computed.
          The default is to compute the mean of the flattened 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 container. If the value is anything
          but the default, then keepdims will be passed through to the mean or
          sum methods of sub-classes of ndarray. If the sub-classes methods
          does not implement keepdims any exceptions will be raised.
      overwrite_input
          If True, then allow use of memory of input array a for calculations.
          The input array will be modified by the call to median.
          This will save memory when you do not need to preserve
          the contents of the input array.Treat the input as undefined,
          but it will probably be fully or partially sorted.
          Default is False. If overwrite_input is True and
          input container does not already have leaves which are
          of the ndarray kind, an error will be raised.
      out
          optional output array, for writing the result to.
  
      Returns
      -------
      ret
          A new array holding the result. If the input contains integers
  
      Examples
      --------
      With :class:`ivy.Container` input and default backend set as `numpy`:
      >>> x = ivy.Container(a=ivy.array([[10.0, ivy.nan, 4], [3, 2, 1]]),
              b=ivy.array([[12, 10, 34], [45, 23, ivy.nan]]))
      >>> x.nanmedian()
      {
          a: ivy.array(3.),
          b: ivy.array(23.)
      }
      >>> x.nanmedian(axis=0)
      {
          a: ivy.array([6.5, 2., 2.5]),
          b: ivy.array([28.5, 16.5, 34.])
      }
      """
      return self.static_nanmedian(
          self,
          axis=axis,
          keepdims=keepdims,
          overwrite_input=overwrite_input,
          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 16 for calling the _ContainerWithStatisticalExperimental obj's underlying member method code with example usage: obj.nanmedian() and returns: self