code: def calculate_range(
      distributed,
      total,
      num_replicas,
      rank,
      num_workers,
      worker_id,
      batch_size,
      drop_last,
      drop_uneven_inputs,
  ):
      """Calculates the range of items to be assigned to the current worker.
  
      This function evenly distributes `total` items among multiple workers,
      batching them using `batch_size`. Each replica has `num_workers` workers.
      The batches generated by workers within the same replica are combined into
      the replica`s output. The `drop_last` parameter determines whether
      incomplete batches should be dropped. If `drop_last` is True, incomplete
      batches are discarded. The `drop_uneven_inputs` parameter determines if the
      number of batches assigned to each replica should be the same. If
      `drop_uneven_inputs` is True, excessive batches for some replicas will be
      dropped.
  
      Args:
          distributed (bool): Whether it's in distributed mode.
          total (int): The total number of items.
          num_replicas (int): The total number of replicas.
          rank (int): The rank of the current replica.
          num_workers (int): The number of workers per replica.
          worker_id (int): The ID of the current worker.
          batch_size (int): The desired batch size.
          drop_last (bool): Whether to drop incomplete batches.
          drop_uneven_inputs (bool): Whether to drop excessive batches for some
            replicas.
  
      Returns:
          tuple: A tuple containing three numbers:
              - start_offset (int): The starting offset of the range assigned to
                the current worker.
              - assigned_count (int): The length of the range assigned to the
                current worker.
              - output_count (int): The number of items that the current worker
                will produce after dropping.
      """
      # Check if it's distributed mode.
      if not distributed:
          if not drop_last:
              return (0, total, total)
          else:
              return (0, total, total // batch_size * batch_size)
      # First, equally distribute items into all replicas.
      assigned_count, start_offset = count_split(
          total, num_replicas, rank, batch_size
      )
      # Calculate the number of outputs when drop_uneven_inputs is True.
      # `assigned_count` is the number of items distributed to the current
      # process. `output_count` is the number of items should be output
      # by this process after dropping.
      if not drop_uneven_inputs:
          if not drop_last:
              output_count = assigned_count
          else:
              output_count = assigned_count // batch_size * batch_size
      else:
          if not drop_last:
              min_item_count, _ = count_split(
                  total, num_replicas, num_replicas - 1, batch_size
              )
              min_batch_count = (
                  min_item_count + batch_size - 1
              ) // batch_size
              output_count = min(
                  min_batch_count * batch_size, assigned_count
              )
          else:
              output_count = (
                  total // (batch_size * num_replicas) * batch_size
              )
      # If there are multiple workers, equally distribute the batches to
      # all workers.
      if num_workers > 1:
          # Equally distribute the dropped number too.
          dropped_items, prev_dropped_items = count_split(
              assigned_count - output_count, num_workers, worker_id
          )
          output_count, prev_output_count = count_split(
              output_count,
              num_workers,
              worker_id,
              batch_size,
          )
          assigned_count = output_count + dropped_items
          start_offset += prev_output_count + prev_dropped_items
      return (start_offset, assigned_count, output_count)
  
label_desc: Use image node_id 2 for calling a global function with example usage: calculate_range(distributed, total, num_replicas, rank, num_workers, worker_id, batch_size, drop_last, drop_uneven_inputs) and returns: start_offset,  assigned_count,  output_count, int,  total,  total, int,  total,  unknown