code: def geometric_brownian_motion_log_returns(
      params: "ModelParameters",
  ) -> "np.array":
      """Constructs a sequence of log returns.
  
      When log returns are exponentiated, it produces a random Geometric
      Brownian Motion (GBM). The GBM is the stochastic process underlying
      the Black-Scholes options pricing formula.
  
      Parameters
      ----------
      params : `ModelParameters`
          The parameters for the stochastic model.
  
      Returns
      -------
      `np.array`
          The log returns of a geometric brownian motion process
      """
      wiener_process = np.array(brownian_motion_log_returns(params))
      sigma_pow_mu_delta = (
          params.gbm_mu - 0.5 * pow(params.all_sigma, 2)
      ) * params.all_delta
      return wiener_process + sigma_pow_mu_delta
  
file_path: tensortrade/tensortrade/stochastic/processes/gbm.py
imports: import numpy
  import pandas
  from stochastic.processes.noise import GaussianNoise
  from tensortrade.stochastic.processes.brownian_motion import brownian_motion_log_returns
  from tensortrade.stochastic.utils.helpers import get_delta, scale_times_to_generate, convert_to_prices
  from tensortrade.stochastic.utils.parameters import ModelParameters, default
label_desc: Use image node_id 1 for calling a global function with example usage: geometric_brownian_motion_log_returns(params) and returns: unknown