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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def expected_h(nvals, fit="RANSAC"):
 """ Uses expected_rs to calculate the expected value for the Hurst exponent h based on the values of n used for the calculation. Args: nvals (iterable of int):
 the values of n used to calculate the individual (R/S)_n KWargs: fit (str):
 the fitting method to use for the line fit, either 'poly' for normal least squares polynomial fitting or 'RANSAC' for RANSAC-fitting which is more robust to outliers Returns: float: expected h for white noise """

  rsvals = [expected_rs(n) for n in nvals]
  poly = poly_fit(np.log(nvals), np.log(rsvals), 1, fit=fit)
  return poly[0]