code: def std(
      a,
      weights=None,
      axis=None,
      dtype=None,
      ddof: int = 0,
      keepdims: bool = False,
  ):
      """
      Compute the weighted standard deviation along the specified axis.
  
      :param a: Array containing numbers whose standard deviation is desired. If `a` is not an
          array, a conversion is attempted.
      :param weights: Array containing weights for the elements of `a`. If `weights` is not an
          array, a conversion is attempted.
      :param axis: Axis or axes along which the means are computed. The default is to
          compute the mean of the flattened array. Type is None or int or tuple of ints, optional.
      :param dtype: data type to use in computing the mean.
      :param int ddof: Delta Degrees of Freedom.  The divisor used in calculations
          is ``W - ddof``, where ``W`` is the sum of weights (or number of elements
          if `weights` is None). By default `ddof` is zero
      :param bool keepdims: If this is set to True, the axes which are reduced are left
          in the result as dimensions with size one.
      :return: np.ndarray
      """
      if weights is None:
          return np.std(
              a, axis=axis, dtype=dtype, ddof=ddof, keepdims=keepdims
          )
      else:
          m = mean(a, weights=weights, axis=axis, keepdims=True)
          v = mean(
              (a - m) ** 2,
              weights=weights,
              axis=axis,
              keepdims=keepdims,
              ddof=ddof,
          )
          return np.sqrt(v)
  
file_path: popmon/popmon/stats/numpy.py
imports: import numpy
  import pandas
label_desc: Use image node_id 2 for calling a global function with example usage: std(a, weights, axis, dtype, ddof, keepdims) and returns: np, np