code: def _calc_n_jobs(df, n_grids, memory_limit, n_jobs):
      """
      Calculate the number of jobs to be executed in parallel based on memory constraints.
  
      Parameters
      ----------
      df : pd.DataFrame
          Input DataFrame.
      n_grids : int
          Number of grid points.
      memory_limit : float
          Fraction of available memory to use.
      n_jobs : int
          Number of jobs specified.
  
      Returns
      -------
      int
          The calculated number of jobs to be executed in parallel.
      """
      _check_memory_limit(memory_limit)
      # df memory in bytes
      unit_memory = df.memory_usage(deep=True).sum()
      # free memory in bytes
      free_memory = psutil.virtual_memory().available * memory_limit
      num_units = int(free_memory // unit_memory)
      true_n_jobs = max(1, min(num_units, n_jobs, n_grids))
      return true_n_jobs
  
imports: import numpy
  import pandas
  import psutil
label_desc: Use image node_id 10 for calling a global function with example usage: _calc_n_jobs(df, n_grids, memory_limit, n_jobs) and returns: true_n_jobs