code: def __calc_z0(x, p_0, statistic, eps, a_hat, sigma_hat):
      """calculate the bias z_0 for abc method.
  
      See Also
      --------
      abc, __tt, __tt_dot, __tt_dot_dot
  
      """
      n = len(x)
      b_hat = np.ones(x.shape)
      tt_dot = np.ones(x.shape)
      for i in range(0, n):
          b_hat[i] = __tt_dot_dot(i, x, p_0, statistic, eps)
          tt_dot[i] = __tt_dot(i, x, p_0, statistic, eps)
      b_hat = b_hat / (2 * n**2)
      c_q_hat = (
          __tt(
              x,
              ((1 - eps) * p_0 + eps * tt_dot / (n**2 * sigma_hat)),
              statistic,
          )
          + __tt(
              x,
              ((1 - eps) * p_0 - eps * tt_dot / (n**2 * sigma_hat)),
              statistic,
          )
          - 2 * __tt(x, p_0, statistic)
      ) / eps**2
      return a_hat - (b_hat / sigma_hat - c_q_hat)
  
file_path: dipy/dipy/boots/resampling.py
imports: import numpy
  import scipy
  import warnings
label_desc: Use image node_id 5 for calling a global function with example usage: __calc_z0(x, p_0, statistic, eps, a_hat, sigma_hat) and returns: unknown