class_docstr: Powers function
  
  :param bounds: the box constraints to define the domain in which the function is optimized.
  :param sd: standard deviation, to generate noisy evaluations of the function.
class_docstr_tok: ["Powers","function",":","param","bounds",":","the","box","constraints","to","define","the","domain","in","which","the","function","is","optimized",".",":","param","sd",":","standard","deviation",",","to","generate","noisy","evaluations","of","the","function","."]
code: def f(self, x):
      x = reshape(x, self.input_dim)
      n = x.shape[0]
      if x.shape[1] != self.input_dim:
          return "wrong input dimension"
      else:
          x1 = x[:, 0]
          x2 = x[:, 1]
          fval = abs(x1) ** 2 + abs(x2) ** 3
          if self.sd == 0:
              noise = np.zeros(n).reshape(n, 1)
          else:
              noise = np.random.normal(0, self.sd, n).reshape(n, 1)
          return fval.reshape(n, 1) + noise
  
imports: import numpy
  from ..util.general import reshape
label_desc: Use image node_id 2 for calling the powers obj's underlying member method code with example usage: obj.f(x) and returns: str, unknown