docstr: ivy.Container instance method variant of ivy.mean. This method
  simply wraps the function, and so the docstring for ivy.mean also
  applies to this method with minimal changes.
  
  Parameters
  ----------
  self
      input container. Should have a floating-point data type.
  axis
      axis or axes along which arithmetic means must be computed. By default,
      the mean must be computed over the entire array. If a Sequence of
      integers, arithmetic means must be computed over multiple axes.
      Default: ``None``.
  keepdims
      bool, if ``True``, the reduced axes (dimensions) must be included in the
      result as singleton dimensions, and, accordingly, the result must be
      compatible with the input array (see :ref:`broadcasting`). Otherwise,
      if ``False``, the reduced axes (dimensions) must not be included in
      the result. Default: ``False``.
  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``.
  out
      optional output, for writing the result to.
      It must have a shape that the inputs broadcast to.
  
  Returns
  -------
  ret
     container, if the arithmetic mean was computed over the entire array,
     a zero-dimensional array containing the arithmetic mean;
     otherwise, a non-zero-dimensional array containing the arithmetic
     means. The returned array must have the same data type as ``self``.
  
  Examples
  --------
  With :class:`ivy.Container` input:
  
  >>> x = ivy.Container(a=ivy.array([0., 1., 2.]), b=ivy.array([3., 4., 5.]))
  >>> y = x.mean()
  >>> print(y)
  {
      a: ivy.array(1.),
      b: ivy.array(4.)
  }
  
  >>> x = ivy.Container(a=ivy.array([0.1, 1.1]), b=ivy.array([0.1, 1.1, 2.1]))
  >>> y = x.mean(keepdims=True)
  >>> print(y)
  {
      a: ivy.array([0.60000002]),
      b: ivy.array([1.10000002])
  }
  
  >>> x = ivy.Container(a=ivy.array([[0.1, 1.1]]), b=ivy.array([[2., 4.]]))
  >>> y = x.mean(axis=1, keepdims=True)
  >>> print(y)
  {
      a: ivy.array([[0.60000002]]),
      b: ivy.array([[3.]])
  }
  
  >>> x = ivy.Container(a=ivy.array([-1., 0., 1.]), b=ivy.array([1.1, 0.2, 1.4]))
  >>> x.mean(out=x)
  >>> print(x)
  {
      a: ivy.array(0.),
      b: ivy.array(0.9)
  }
  
  >>> x = ivy.Container(a=ivy.array([0., -1., 1.]), b=ivy.array([1., 1., 1.]))
  >>> y = ivy.Container(a=ivy.array(0.), b=ivy.array(0.))
  >>> x.mean(out=y)
  >>> print(y)
  {
      a: ivy.array(0.),
      b: ivy.array(1.)
  }
  
  >>> x = ivy.Container(a=ivy.array([[0., 1., 2.], [3., 4., 5.]]),
  ...                   b=ivy.array([[3., 4., 5.], [6., 7., 8.]]))
  >>> x.mean(axis=0, out=x)
  >>> print(x)
  {
      a: ivy.array([1.5, 2.5, 3.5]),
      b: ivy.array([4.5, 5.5, 6.5])
  }
  
  >>> x = ivy.Container(a=ivy.array([[1., 1., 1.], [2., 2., 2.]]),
  ...                   b=ivy.array([[3., 3., 3.], [4., 4., 4.]]))
  >>> y = ivy.mean(x, axis=1)
  >>> print(y)
  {
      a: ivy.array([1., 2.]),
      b: ivy.array([3., 4.])
  }
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code: def mean(
      self: ivy.Container,
      /,
      *,
      axis: Optional[Union[int, Sequence[int], ivy.Container]] = None,
      keepdims: Union[bool, ivy.Container] = False,
      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 instance method variant of ivy.mean. This method
      simply wraps the function, and so the docstring for ivy.mean also
      applies to this method with minimal changes.
  
      Parameters
      ----------
      self
          input container. Should have a floating-point data type.
      axis
          axis or axes along which arithmetic means must be computed. By default,
          the mean must be computed over the entire array. If a Sequence of
          integers, arithmetic means must be computed over multiple axes.
          Default: ``None``.
      keepdims
          bool, if ``True``, the reduced axes (dimensions) must be included in the
          result as singleton dimensions, and, accordingly, the result must be
          compatible with the input array (see :ref:`broadcasting`). Otherwise,
          if ``False``, the reduced axes (dimensions) must not be included in
          the result. Default: ``False``.
      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``.
      out
          optional output, for writing the result to.
          It must have a shape that the inputs broadcast to.
  
      Returns
      -------
      ret
         container, if the arithmetic mean was computed over the entire array,
         a zero-dimensional array containing the arithmetic mean;
         otherwise, a non-zero-dimensional array containing the arithmetic
         means. The returned array must have the same data type as ``self``.
  
      Examples
      --------
      With :class:`ivy.Container` input:
  
      >>> x = ivy.Container(a=ivy.array([0., 1., 2.]), b=ivy.array([3., 4., 5.]))
      >>> y = x.mean()
      >>> print(y)
      {
          a: ivy.array(1.),
          b: ivy.array(4.)
      }
  
      >>> x = ivy.Container(a=ivy.array([0.1, 1.1]), b=ivy.array([0.1, 1.1, 2.1]))
      >>> y = x.mean(keepdims=True)
      >>> print(y)
      {
          a: ivy.array([0.60000002]),
          b: ivy.array([1.10000002])
      }
  
      >>> x = ivy.Container(a=ivy.array([[0.1, 1.1]]), b=ivy.array([[2., 4.]]))
      >>> y = x.mean(axis=1, keepdims=True)
      >>> print(y)
      {
          a: ivy.array([[0.60000002]]),
          b: ivy.array([[3.]])
      }
  
      >>> x = ivy.Container(a=ivy.array([-1., 0., 1.]), b=ivy.array([1.1, 0.2, 1.4]))
      >>> x.mean(out=x)
      >>> print(x)
      {
          a: ivy.array(0.),
          b: ivy.array(0.9)
      }
  
      >>> x = ivy.Container(a=ivy.array([0., -1., 1.]), b=ivy.array([1., 1., 1.]))
      >>> y = ivy.Container(a=ivy.array(0.), b=ivy.array(0.))
      >>> x.mean(out=y)
      >>> print(y)
      {
          a: ivy.array(0.),
          b: ivy.array(1.)
      }
  
      >>> x = ivy.Container(a=ivy.array([[0., 1., 2.], [3., 4., 5.]]),
      ...                   b=ivy.array([[3., 4., 5.], [6., 7., 8.]]))
      >>> x.mean(axis=0, out=x)
      >>> print(x)
      {
          a: ivy.array([1.5, 2.5, 3.5]),
          b: ivy.array([4.5, 5.5, 6.5])
      }
  
      >>> x = ivy.Container(a=ivy.array([[1., 1., 1.], [2., 2., 2.]]),
      ...                   b=ivy.array([[3., 3., 3.], [4., 4., 4.]]))
      >>> y = ivy.mean(x, axis=1)
      >>> print(y)
      {
          a: ivy.array([1., 2.]),
          b: ivy.array([3., 4.])
      }
      """
      return self.cont_handle_inplace(
          self.cont_map(
              lambda x_, _: (
                  ivy.mean(x_, axis=axis, keepdims=keepdims)
                  if ivy.is_array(x_)
                  else x_
              ),
              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, Sequence
  import ivy
  from ivy.data_classes.container.base import ContainerBase
label_desc: Use image node_id 4 for calling the _ContainerWithStatistical obj's underlying member method code with example usage: obj.mean() and returns: self