docstr: ivy.Container 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.
  
  Parameters
  ----------
  self
      input container. 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 for each array in the input
      container. 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``.
      input array. Should have a floating-point data type.
  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
      a container containing different arrays depends on parameters. see below
      for the types of arrays in the returned container 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 container must have the same data type as self.
  
  Examples
  --------
  >>> x = ivy.Container(a=ivy.array([0.0, 1.0, 2.0]),
  ...                   b=ivy.array([3.0, 4.0, 5.0]))
  >>> y = x.var()
  >>> print(y)
  {
      a: ivy.array(0.6666667),
      b: ivy.array(0.6666667)
  }
  
  >>> x = ivy.Container(a=ivy.array([0.0, 1.0, 2.0]),
  ...                   b=ivy.array([3.0, 4.0, 5.0]))
  >>> y = ivy.Container(a=ivy.array(0.), b=ivy.array(0.))
  >>> x.var(out=y)
  >>> print(y)
  {
      a: ivy.array(0.6666667),
      b: ivy.array(0.6666667)
  }
  
  >>> x = ivy.Container(a=ivy.array([[0.0, 1.0, 2.0], [3.0, 4.0, 5.0]]),
  ...                   b=ivy.array([[6.0, 7.0, 8.0], [9.0, 10.0, 11.0]]))
  >>> y = ivy.Container(a=ivy.array([0., 0., 0.]), b=ivy.array([0., 0., 0.]))
  >>> x.var(axis=0, out=y)
  >>> print(y)
  {
      a: ivy.array([2.25, 2.25, 2.25]),
      b: ivy.array([2.25, 2.25, 2.25])
  }
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code: def var(
      self: ivy.Container,
      /,
      *,
      axis: Optional[Union[int, Sequence[int], ivy.Container]] = None,
      correction: Union[int, float, ivy.Container] = 0.0,
      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.var. This method simply
      wraps the function, and so the docstring for ivy.var 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 variances must be computed. By default, the
          variance must be computed over the entire array for each array in the input
          container. 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``.
          input array. Should have a floating-point data type.
      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
          a container containing different arrays depends on parameters. see below
          for the types of arrays in the returned container 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 container must have the same data type as self.
  
      Examples
      --------
      >>> x = ivy.Container(a=ivy.array([0.0, 1.0, 2.0]),
      ...                   b=ivy.array([3.0, 4.0, 5.0]))
      >>> y = x.var()
      >>> print(y)
      {
          a: ivy.array(0.6666667),
          b: ivy.array(0.6666667)
      }
  
      >>> x = ivy.Container(a=ivy.array([0.0, 1.0, 2.0]),
      ...                   b=ivy.array([3.0, 4.0, 5.0]))
      >>> y = ivy.Container(a=ivy.array(0.), b=ivy.array(0.))
      >>> x.var(out=y)
      >>> print(y)
      {
          a: ivy.array(0.6666667),
          b: ivy.array(0.6666667)
      }
  
      >>> x = ivy.Container(a=ivy.array([[0.0, 1.0, 2.0], [3.0, 4.0, 5.0]]),
      ...                   b=ivy.array([[6.0, 7.0, 8.0], [9.0, 10.0, 11.0]]))
      >>> y = ivy.Container(a=ivy.array([0., 0., 0.]), b=ivy.array([0., 0., 0.]))
      >>> x.var(axis=0, out=y)
      >>> print(y)
      {
          a: ivy.array([2.25, 2.25, 2.25]),
          b: ivy.array([2.25, 2.25, 2.25])
      }
      """
      return self.cont_handle_inplace(
          self.cont_map(
              lambda x_, _: (
                  ivy.var(
                      x_,
                      axis=axis,
                      correction=correction,
                      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 5 for calling the _ContainerWithStatistical obj's underlying member method code with example usage: obj.var() and returns: self