code: def cox(
      base_price: int = 1,
      base_volume: int = 1,
      start_date: str = "2010-01-01",
      start_date_format: str = "%Y-%m-%d",
      times_to_generate: int = 1000,
      time_frame: str = "1h",
      params: "ModelParameters" = None,
  ) -> "pd.DataFrame":
      """Generates price data from the CIR process.
  
      Parameters
      ----------
      base_price : int, default 1
          The base price to use for price generation.
      base_volume : int, default 1
          The base volume to use for volume generation.
      start_date : str, default '2010-01-01'
          The start date of the generated data
      start_date_format : str, default '%Y-%m-%d'
          The format for the start date of the generated data.
      times_to_generate : int, default 1000
          The number of bars to make.
      time_frame : str, default '1h'
          The time frame.
      params : `ModelParameters`, optional
          The model parameters.
  
      Returns
      -------
      `pd.DataFrame`
          The generated data frame containing the OHLCV bars.
  
      References
      ----------
      [1] https://en.wikipedia.org/wiki/Cox%E2%80%93Ingersoll%E2%80%93Ross_model
      """
  
      data_frame = generate(
          price_fn=cox_ingersoll_ross_levels,
          base_price=base_price,
          base_volume=base_volume,
          start_date=start_date,
          start_date_format=start_date_format,
          times_to_generate=times_to_generate,
          time_frame=time_frame,
          params=params,
      )
  
      return data_frame
  
imports: import numpy
  import pandas
  from tensortrade.stochastic.processes.brownian_motion import brownian_motion_log_returns
  from tensortrade.stochastic.utils import ModelParameters, generate
label_desc: Use image node_id 2 for calling a global function with example usage: cox(base_price, base_volume, start_date, start_date_format, times_to_generate, time_frame, params) and returns: data_frame