code: public SDVariable standardDeviation(String name, SDVariable x, boolean biasCorrected, boolean keepDims, int... dimensions) {
          validateNumerical("standard deviation", x);
          SDVariable result = f().std(x, biasCorrected, keepDims, dimensions);
          return updateVariableNameAndReference(result, name);
      }
docs: Stardard deviation array reduction operation, optionally along specified dimensions<br>
  Note that if keepDims = true, the output variable has the same rank as the input variable,
  with the reduced dimensions having size 1. This can be useful for later broadcast operations (such as subtracting
  the mean along a dimension).<br>
  Example: if input has shape [a,b,c] and dimensions=[1] then output has shape:
  keepDims = true: [a,1,c]<br>
  keepDims = false: [a,c]
  
  @param x             Input variable
  @param biasCorrected If true: divide by (N-1) (i.e., sample stdev). If false: divide by N (population stdev)
  @param keepDims      If true: keep the dimensions that are reduced on (as size 1). False: remove the reduction dimensions
  @param dimensions    Dimensions to reduce over. If dimensions are not specified, full array reduction is performed
  @return Output variable: reduced array of rank (input rank - num dimensions)