docstr: ivy.Container 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 container.
  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.
  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 standard deviation was computed over the
      entire array, a zero-dimensional array containing the
      standard deviation; otherwise, a non-zero-dimensional array
      containing the respectve standard deviations. The returned
      array must have the same data type as ``self``.
  
  Examples
  --------
  With :class:`ivy.Container` input:
  
  >>> x = ivy.Container(a=ivy.array([0., 2.]), b=ivy.array([-4., 5.]))
  >>> y = x.std()
  >>> print(y)
  {
      a: ivy.array(1.),
      b: ivy.array(4.5)
  }
  
  >>> x = ivy.Container(a=ivy.array([0.1, 1.1]), b=ivy.array([0.1, 1.1, 2.1]))
  >>> y = x.std(keepdims=True)
  >>> print(y)
  {
      a: ivy.array([0.5]),
      b: ivy.array([0.81649649])
  }
  
  >>> x = ivy.Container(a=ivy.array([[2., 1.]]), b=ivy.array([[2., -2.]]))
  >>> y = x.std(axis=1, keepdims=True)
  >>> print(y)
  {
      a: ivy.array([[0.5]]),
      b: ivy.array([[2.]])
  }
  
  >>> x = ivy.Container(a=ivy.array([-1., 1., 1.]), b=ivy.array([1.1, 0.2, 1.4]))
  >>> x.std(out=x)
  >>> print(x)
  {
      a: ivy.array(0.94280904),
      b: ivy.array(0.509902)
  }
  
  >>> x = ivy.Container(a=ivy.array([0., -2., 1.]), b=ivy.array([1., 1., 1.]))
  >>> y = ivy.Container(a=ivy.array(0.), b=ivy.array(0.))
  >>> x.std(out=y)
  >>> print(y)
  {
      a: ivy.array(1.2472192),
      b: ivy.array(0.)
  }
  
  >>> x = ivy.Container(a=ivy.array([[-1., 1., 2.], [2., 2., 2.]]),
  ...                   b=ivy.array([[3., 0., -3.], [4., 1., 4.]]))
  >>> y = x.std(axis=1)
  >>> print(y)
  {
      a: ivy.array([1.2472192, 0.]),
      b: ivy.array([2.44948983, 1.41421354])
  }
docstr_tok: ["ivy.Container","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","container",".","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",".","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","standard","deviation","was","computed","over","the","entire","array",",","a","zero-dimensional","array","containing","the","standard","deviation",";","otherwise",",","a","non-zero-dimensional","array","containing","the","respectve","standard","deviations",".","The","returned","array","must","have","the","same","data","type","as","``","self","``",".","Examples","--","--","--","--","With",":","class",":","`","ivy.Container","`","input",":",">",">",">","x","=","ivy.Container","(","a=ivy.array","(","[","0.",",","2",".","]",")",",","b=ivy.array","(","[","-4.",",","5",".","]",")",")",">",">",">","y","=","x.std","(",")",">",">",">","print","(","y",")","{","a",":","ivy.array","(","1",".",")",",","b",":","ivy.array","(","4.5",")","}",">",">",">","x","=","ivy.Container","(","a=ivy.array","(","[","0.1",",","1.1","]",")",",","b=ivy.array","(","[","0.1",",","1.1",",","2.1","]",")",")",">",">",">","y","=","x.std","(","keepdims=True",")",">",">",">","print","(","y",")","{","a",":","ivy.array","(","[","0.5","]",")",",","b",":","ivy.array","(","[","0.81649649","]",")","}",">",">",">","x","=","ivy.Container","(","a=ivy.array","(","[","[","2.",",","1",".","]","]",")",",","b=ivy.array","(","[","[","2.",",","-2",".","]","]",")",")",">",">",">","y","=","x.std","(","axis=1",",","keepdims=True",")",">",">",">","print","(","y",")","{","a",":","ivy.array","(","[","[","0.5","]","]",")",",","b",":","ivy.array","(","[","[","2",".","]","]",")","}",">",">",">","x","=","ivy.Container","(","a=ivy.array","(","[","-1.",",","1.",",","1",".","]",")",",","b=ivy.array","(","[","1.1",",","0.2",",","1.4","]",")",")",">",">",">","x.std","(","out=x",")",">",">",">","print","(","x",")","{","a",":","ivy.array","(","0.94280904",")",",","b",":","ivy.array","(","0.509902",")","}",">",">",">","x","=","ivy.Container","(","a=ivy.array","(","[","0.",",","-2.",",","1",".","]",")",",","b=ivy.array","(","[","1.",",","1.",",","1",".","]",")",")",">",">",">","y","=","ivy.Container","(","a=ivy.array","(","0",".",")",",","b=ivy.array","(","0",".",")",")",">",">",">","x.std","(","out=y",")",">",">",">","print","(","y",")","{","a",":","ivy.array","(","1.2472192",")",",","b",":","ivy.array","(","0",".",")","}",">",">",">","x","=","ivy.Container","(","a=ivy.array","(","[","[","-1.",",","1.",",","2",".","]",",","[","2.",",","2.",",","2",".","]","]",")",",","...","b=ivy.array","(","[","[","3.",",","0.",",","-3",".","]",",","[","4.",",","1.",",","4",".","]","]",")",")",">",">",">","y","=","x.std","(","axis=1",")",">",">",">","print","(","y",")","{","a",":","ivy.array","(","[","1.2472192",",","0",".","]",")",",","b",":","ivy.array","(","[","2.44948983",",","1.41421354","]",")","}"]
code: def std(
      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.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 container.
      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.
      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 standard deviation was computed over the
          entire array, a zero-dimensional array containing the
          standard deviation; otherwise, a non-zero-dimensional array
          containing the respectve standard deviations. The returned
          array must have the same data type as ``self``.
  
      Examples
      --------
      With :class:`ivy.Container` input:
  
      >>> x = ivy.Container(a=ivy.array([0., 2.]), b=ivy.array([-4., 5.]))
      >>> y = x.std()
      >>> print(y)
      {
          a: ivy.array(1.),
          b: ivy.array(4.5)
      }
  
      >>> x = ivy.Container(a=ivy.array([0.1, 1.1]), b=ivy.array([0.1, 1.1, 2.1]))
      >>> y = x.std(keepdims=True)
      >>> print(y)
      {
          a: ivy.array([0.5]),
          b: ivy.array([0.81649649])
      }
  
      >>> x = ivy.Container(a=ivy.array([[2., 1.]]), b=ivy.array([[2., -2.]]))
      >>> y = x.std(axis=1, keepdims=True)
      >>> print(y)
      {
          a: ivy.array([[0.5]]),
          b: ivy.array([[2.]])
      }
  
      >>> x = ivy.Container(a=ivy.array([-1., 1., 1.]), b=ivy.array([1.1, 0.2, 1.4]))
      >>> x.std(out=x)
      >>> print(x)
      {
          a: ivy.array(0.94280904),
          b: ivy.array(0.509902)
      }
  
      >>> x = ivy.Container(a=ivy.array([0., -2., 1.]), b=ivy.array([1., 1., 1.]))
      >>> y = ivy.Container(a=ivy.array(0.), b=ivy.array(0.))
      >>> x.std(out=y)
      >>> print(y)
      {
          a: ivy.array(1.2472192),
          b: ivy.array(0.)
      }
  
      >>> x = ivy.Container(a=ivy.array([[-1., 1., 2.], [2., 2., 2.]]),
      ...                   b=ivy.array([[3., 0., -3.], [4., 1., 4.]]))
      >>> y = x.std(axis=1)
      >>> print(y)
      {
          a: ivy.array([1.2472192, 0.]),
          b: ivy.array([2.44948983, 1.41421354])
      }
      """
      return self.cont_handle_inplace(
          self.cont_map(
              lambda x_, _: (
                  ivy.std(
                      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 11 for calling the _ContainerWithStatistical obj's underlying member method code with example usage: obj.std() and returns: self