docstr: ivy.Array instance method variant of ivy.var. This method simply
  wraps the function, and so the docstring for ivy.var also applies to
  this method with minimal changes.
  
  **Special Cases**
  
  Let N equal the number of elements over which to compute the variance.
  
  If N - correction is less than or equal to 0, the variance is NaN.
  
  If x_i is NaN, the variance is NaN (i.e., NaN values propagate).
  
  Parameters
  ----------
  self
      input array. Should have a floating-point data type.
  axis
      axis or axes along which variances must be computed. By default, the
      variance must be computed over the entire array. If a tuple of integers,
      variances must be computed over multiple axes. Default: ``None``.
  correction
      degrees of freedom adjustment. Setting this parameter to a value other
      than 0 has the effect of adjusting the divisor during the calculation
      of the variance according to N-c where N corresponds to the total
      number of elements over which the variance is computed and c corresponds
      to the provided degrees of freedom adjustment. When computing the variance
      of a population, setting this parameter to 0 is the standard choice
      (i.e., the provided array contains data constituting an entire population).
      When computing the unbiased sample variance, setting this parameter to 1
      is the standard choice (i.e., the provided array contains data sampled
      from a larger population; this is commonly referred to as Bessel's
      correction). Default: ``0``.
  keepdims
      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 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
      if the variance was computed over the entire array, a zero-dimensional array
      containing the variance; otherwise, a non-zero-dimensional array containing
      the variances. The returned array must have the same data type as x.
  
  Examples
  --------
  >>> x = ivy.array([[0.0, 1.0, 2.0],
  ...                [3.0, 4.0, 5.0],
  ...                [6.0, 7.0, 8.0]])
  >>> y = x.var()
  >>> print(y)
  ivy.array(6.6666665)
  
  >>> x = ivy.array([[0.0, 1.0, 2.0],
  ...                [3.0, 4.0, 5.0],
  ...                [6.0, 7.0, .08]])
  >>> y = x.var(axis=0)
  >>> print(y)
  ivy.array([6., 6., 4.1])
  
  >>> x = ivy.array([[0.0, 1.0, 2.0],
  ...                [3.0, 4.0, 5.0],
  ...                [6.0, 7.0, .08]])
  >>> y = ivy.array([0., 0., 0.])
  >>> x.var(axis=1, out=y)
  >>> print(y)
  ivy.array([0.667, 0.667, 9.33 ])
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code: def var(
      self: ivy.Array,
      /,
      *,
      axis: Optional[Union[int, Sequence[int]]] = None,
      correction: Union[int, float] = 0.0,
      keepdims: bool = False,
      out: Optional[ivy.Array] = None,
  ) -> ivy.Array:
      """ivy.Array instance method variant of ivy.var. This method simply
      wraps the function, and so the docstring for ivy.var also applies to
      this method with minimal changes.
  
      **Special Cases**
  
      Let N equal the number of elements over which to compute the variance.
  
      If N - correction is less than or equal to 0, the variance is NaN.
  
      If x_i is NaN, the variance is NaN (i.e., NaN values propagate).
  
      Parameters
      ----------
      self
          input array. Should have a floating-point data type.
      axis
          axis or axes along which variances must be computed. By default, the
          variance must be computed over the entire array. If a tuple of integers,
          variances must be computed over multiple axes. Default: ``None``.
      correction
          degrees of freedom adjustment. Setting this parameter to a value other
          than 0 has the effect of adjusting the divisor during the calculation
          of the variance according to N-c where N corresponds to the total
          number of elements over which the variance is computed and c corresponds
          to the provided degrees of freedom adjustment. When computing the variance
          of a population, setting this parameter to 0 is the standard choice
          (i.e., the provided array contains data constituting an entire population).
          When computing the unbiased sample variance, setting this parameter to 1
          is the standard choice (i.e., the provided array contains data sampled
          from a larger population; this is commonly referred to as Bessel's
          correction). Default: ``0``.
      keepdims
          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 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
          if the variance was computed over the entire array, a zero-dimensional array
          containing the variance; otherwise, a non-zero-dimensional array containing
          the variances. The returned array must have the same data type as x.
  
      Examples
      --------
      >>> x = ivy.array([[0.0, 1.0, 2.0],
      ...                [3.0, 4.0, 5.0],
      ...                [6.0, 7.0, 8.0]])
      >>> y = x.var()
      >>> print(y)
      ivy.array(6.6666665)
  
      >>> x = ivy.array([[0.0, 1.0, 2.0],
      ...                [3.0, 4.0, 5.0],
      ...                [6.0, 7.0, .08]])
      >>> y = x.var(axis=0)
      >>> print(y)
      ivy.array([6., 6., 4.1])
  
      >>> x = ivy.array([[0.0, 1.0, 2.0],
      ...                [3.0, 4.0, 5.0],
      ...                [6.0, 7.0, .08]])
      >>> y = ivy.array([0., 0., 0.])
      >>> x.var(axis=1, out=y)
      >>> print(y)
      ivy.array([0.667, 0.667, 9.33 ])
      """
      return ivy.var(
          self._data,
          axis=axis,
          correction=correction,
          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 4 for calling the _ArrayWithStatistical obj's underlying member method code with example usage: obj.var() and returns: ivy