code: def heston_construct_correlated_path(
      params: "ModelParameters", brownian_motion_one: "np.array"
  ) -> "np.array":
      """A simplified version of the Cholesky decomposition method for just two
      assets. It does not make use of matrix algebra and is therefore quite easy
      to implement.
  
      Parameters
      ----------
      params : ModelParameters
          The parameters for the stochastic model.
      brownian_motion_one : `np.array`
              (Not filled)
  
      Returns
      -------
      `np.array`
          A correlated brownian motion path.
      """
      # We do not multiply by sigma here, we do that in the Heston model
      sqrt_delta = np.sqrt(params.all_delta)
      # Construct a path correlated to the first path
      brownian_motion_two = []
      for i in range(params.all_time - 1):
          term_one = params.cir_rho * brownian_motion_one[i]
          term_two = np.sqrt(
              1 - pow(params.cir_rho, 2)
          ) * random.normalvariate(0, sqrt_delta)
          brownian_motion_two.append(term_one + term_two)
      return np.array(brownian_motion_one), np.array(
          brownian_motion_two
      )
  
file_path: tensortrade/tensortrade/stochastic/processes/heston.py
imports: import random
  import numpy
  import pandas
  import scipy
  from tensortrade.stochastic.processes.gbm import geometric_brownian_motion_log_returns
  from tensortrade.stochastic.utils import ModelParameters, generate, convert_to_prices
label_desc: Use image node_id 5 for calling a global function with example usage: heston_construct_correlated_path(params, brownian_motion_one) and returns: np,  np