docstr: ivy.array instance method variant of ivy.std. This method simply
  wraps the function, and so the docstring for ivy.std also applies to
  this method with minimal changes.
  
  Parameters
  ----------
  self
      input array.
  axis
      axis or axes along which standard deviation must be computed.
      By default, the product must be computed over the entire array.
      If a tuple of integers, products 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 standard deviation
      according to ``N-c`` where ``N`` corresponds to the total
      number of elements over which the standard deviation is
      computed and ``c`` corresponds to the provided degrees of
      freedom adjustment. When computing the standard deviation
      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 corrected sample standard deviation, 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
      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 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
      container, if the product was computed over the entire array,
      a zero-dimensional array containing the product;
      otherwise, a non-zero-dimensional array containing the products.
      The returned array must have the same data type as ``self``.
  
  Examples
  --------
  With: class: `ivy.Array` input:
  
  >>> x = ivy.array([-1., 0., 1.])
  >>> y = x.std()
  >>> print(y)
  ivy.array(0.81649661)
  
  >>> x = ivy.array([-1., 0., 1.])
  >>> z = x.std(correction=1)
  >>> print(z)
  ivy.array(1.)
  
  >>> x = ivy.array([[0., 4.]])
  >>> y = x.std(keepdims=True)
  >>> print(y)
  ivy.array([[2.]])
  
  >>> x = ivy.array([2., 1.])
  >>> y = ivy.array(0.)
  >>> x.std(out=y)
  >>> print(y)
  ivy.array(0.5)
  
  >>> x = ivy.array([[-1., -2.], [3., 3.]])
  >>> y = x.std(axis=1)
  >>> print(y)
  ivy.array([0.5, 0. ])
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code: def std(
      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.std. This method simply
      wraps the function, and so the docstring for ivy.std also applies to
      this method with minimal changes.
  
      Parameters
      ----------
      self
          input array.
      axis
          axis or axes along which standard deviation must be computed.
          By default, the product must be computed over the entire array.
          If a tuple of integers, products 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 standard deviation
          according to ``N-c`` where ``N`` corresponds to the total
          number of elements over which the standard deviation is
          computed and ``c`` corresponds to the provided degrees of
          freedom adjustment. When computing the standard deviation
          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 corrected sample standard deviation, 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
          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 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
          container, if the product was computed over the entire array,
          a zero-dimensional array containing the product;
          otherwise, a non-zero-dimensional array containing the products.
          The returned array must have the same data type as ``self``.
  
      Examples
      --------
      With: class: `ivy.Array` input:
  
      >>> x = ivy.array([-1., 0., 1.])
      >>> y = x.std()
      >>> print(y)
      ivy.array(0.81649661)
  
      >>> x = ivy.array([-1., 0., 1.])
      >>> z = x.std(correction=1)
      >>> print(z)
      ivy.array(1.)
  
      >>> x = ivy.array([[0., 4.]])
      >>> y = x.std(keepdims=True)
      >>> print(y)
      ivy.array([[2.]])
  
      >>> x = ivy.array([2., 1.])
      >>> y = ivy.array(0.)
      >>> x.std(out=y)
      >>> print(y)
      ivy.array(0.5)
  
      >>> x = ivy.array([[-1., -2.], [3., 3.]])
      >>> y = x.std(axis=1)
      >>> print(y)
      ivy.array([0.5, 0. ])
      """
      return ivy.std(
          self,
          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 7 for calling the _ArrayWithStatistical obj's underlying member method code with example usage: obj.std() and returns: ivy