code: def stationary_bootstrap_sample_python(
      indices: Int64Array, u: Float64Array, p: float
  ) -> Int64Array:
      """
      Generate indices for sampling from the stationary bootstrap.
  
      Parameters
      -------
      indices: ndarray
          Single-dimensional array containing draws from randint with the same
          size as the data in the range of [0,nobs).
      u : ndarray
          Single-dimensional Array of standard uniforms.
      p : float
          Probability that a new block is started in the stationary bootstrap.
          The multiplicative reciprocal of the window length.
  
      Returns
      -------
      ndarray
          Indices for an iteration of the stationary bootstrap.
      """
      num_items = indices.shape[0]
      for i in range(1, num_items):
          if u[i] > p:
              indices[i] = indices[i - 1] + 1
              if indices[i] == num_items:
                  indices[i] = 0
  
      return indices
  
imports: from __future__ import annotations
  from arch.compat.numba import jit
  from arch.typing import Float64Array, Int64Array
label_desc: Use image node_id 1 for calling a global function with example usage: stationary_bootstrap_sample_python(indices, u, p) and returns: indices