docstr: ivy.Array 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.
  
  **Special Cases**
  
  Let ``N`` equal the number of elements over which to compute the
  arithmetic mean.
  -   If ``N`` is ``0``, the arithmetic mean is ``NaN``.
  -   If ``x_i`` is ``NaN``, the arithmetic mean is ``NaN`` (i.e., ``NaN``
      values propagate).
  
  Parameters
  ----------
  self
      input array. 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``.
  out
      optional output array, for writing the result to.
  
  Returns
  -------
  ret
      array, 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 ``x``.
  
  Examples
  --------
  With :class:`ivy.Array` input:
  
  >>> x = ivy.array([3., 4., 5.])
  >>> y = x.mean()
  >>> print(y)
  ivy.array(4.)
  
  >>> x = ivy.array([-1., 0., 1.])
  >>> y = ivy.mean(x)
  >>> print(y)
  ivy.array(0.)
  
  >>> x = ivy.array([0.1, 1.1, 2.1])
  >>> y = ivy.array(0.)
  >>> x.mean(out=y)
  >>> print(y)
  ivy.array(1.1)
  
  >>> x = ivy.array([1., 2., 3., 0., -1.])
  >>> y = ivy.array(0.)
  >>> ivy.mean(x, out=y)
  >>> print(y)
  ivy.array(1.)
  
  >>> x = ivy.array([[-0.5, 1., 2.], [0.0, 1.1, 2.2]])
  >>> y = ivy.zeros((1, 3))
  >>> x.mean(axis=0, keepdims=True, out=y)
  >>> print(y)
  ivy.array([[-0.25      ,  1.04999995,  2.0999999 ]])
  
  >>> x = ivy.array([[0., 1., 2.], [3., 4., 5.]])
  >>> y = ivy.array([0., 0.])
  >>> ivy.mean(x, axis=1, out=y)
  >>> print(y)
  ivy.array([1., 4.])
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code: def mean(
      self: ivy.Array,
      /,
      *,
      axis: Optional[Union[int, Sequence[int]]] = None,
      keepdims: bool = False,
      out: Optional[ivy.Array] = None,
  ) -> ivy.Array:
      """ivy.Array 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.
  
      **Special Cases**
  
      Let ``N`` equal the number of elements over which to compute the
      arithmetic mean.
      -   If ``N`` is ``0``, the arithmetic mean is ``NaN``.
      -   If ``x_i`` is ``NaN``, the arithmetic mean is ``NaN`` (i.e., ``NaN``
          values propagate).
  
      Parameters
      ----------
      self
          input array. 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``.
      out
          optional output array, for writing the result to.
  
      Returns
      -------
      ret
          array, 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 ``x``.
  
      Examples
      --------
      With :class:`ivy.Array` input:
  
      >>> x = ivy.array([3., 4., 5.])
      >>> y = x.mean()
      >>> print(y)
      ivy.array(4.)
  
      >>> x = ivy.array([-1., 0., 1.])
      >>> y = ivy.mean(x)
      >>> print(y)
      ivy.array(0.)
  
      >>> x = ivy.array([0.1, 1.1, 2.1])
      >>> y = ivy.array(0.)
      >>> x.mean(out=y)
      >>> print(y)
      ivy.array(1.1)
  
      >>> x = ivy.array([1., 2., 3., 0., -1.])
      >>> y = ivy.array(0.)
      >>> ivy.mean(x, out=y)
      >>> print(y)
      ivy.array(1.)
  
      >>> x = ivy.array([[-0.5, 1., 2.], [0.0, 1.1, 2.2]])
      >>> y = ivy.zeros((1, 3))
      >>> x.mean(axis=0, keepdims=True, out=y)
      >>> print(y)
      ivy.array([[-0.25      ,  1.04999995,  2.0999999 ]])
  
      >>> x = ivy.array([[0., 1., 2.], [3., 4., 5.]])
      >>> y = ivy.array([0., 0.])
      >>> ivy.mean(x, axis=1, out=y)
      >>> print(y)
      ivy.array([1., 4.])
      """
      return ivy.mean(self._data, axis=axis, keepdims=keepdims, out=out)
  
file_path: ivy/ivy/data_classes/array/statistical.py
imports: from typing import Optional, Union, Sequence
  import abc
  import ivy
label_desc: Use image node_id 3 for calling the _ArrayWithStatistical obj's underlying member method code with example usage: obj.mean() and returns: ivy