code: def get_rendered_batch_size_grad_accum(
      config: "BaseTrainerConfig", num_workers: int
  ) -> Tuple[int, int]:
      """Returns the batch size and gradient accumulation steps to use for training.
  
      For batch_size==AUTO:
      1. effective_batch_size is not AUTO and gradient_accumulation_steps is not AUTO:
          batch size is set to the effective batch size divided by the gradient accumulation steps, divided by the
          number of workers.
      2. effective_batch_size is AUTO or gradient_accumulation_steps is AUTO:
          batch size remains AUTO.
  
      For gradient_accumulation_steps==AUTO:
      1. batch size is AUTO:
          gradient accumulation steps remains AUTO.
      2. batch_size is not AUTO and effective batch size is not AUTO:
          gradient accumulation steps is set to the effective batch size divided by the batch size, divided by the number
          of workers.
      3. batch size is not AUTO and effective batch size is AUTO:
          gradient accumulation steps is set to 1.
      """
      effective_batch_size = config.effective_batch_size
      batch_size = config.batch_size
      gradient_accumulation_steps = config.gradient_accumulation_steps
  
      if config.batch_size == AUTO:
          if (
              config.effective_batch_size != AUTO
              and config.gradient_accumulation_steps != AUTO
          ):
              batch_size = max(
                  int(
                      effective_batch_size
                      / gradient_accumulation_steps
                      / num_workers
                  ),
                  1,
              )
  
      if config.gradient_accumulation_steps == AUTO:
          if config.batch_size != AUTO:
              if config.effective_batch_size != AUTO:
                  gradient_accumulation_steps = max(
                      int(
                          effective_batch_size
                          / batch_size
                          / num_workers
                      ),
                      1,
                  )
              else:
                  gradient_accumulation_steps = 1
  
      return batch_size, gradient_accumulation_steps
  
imports: import logging
  from collections import defaultdict
  from typing import Dict, List, Tuple, TYPE_CHECKING
  from ludwig.api_annotations import DeveloperAPI
  from ludwig.constants import AUTO, COMBINED, LOSS
  from ludwig.models.base import BaseModel
  from ludwig.modules.metric_modules import get_best_function
  from ludwig.utils.data_utils import save_json
  from ludwig.utils.metric_utils import TrainerMetric
label_desc: Use image node_id 9 for calling a global function with example usage: get_rendered_batch_size_grad_accum(config, num_workers) and returns: batch_size,  gradient_accumulation_steps