code: def _combine_mean(
      pred_results,
      local_results,
      axis=0,
      alpha=None,
      alpha_ignore_na=False,
      pred_exponent=None,
  ):
      if pred_results is None:
          return (local_results[0] / local_results[1]).ffill()
  
      alpha_data = local_results[1]
      local_results[0] = local_results[0].ffill()
      local_results[1] = alpha_data.ffill()
  
      local_sum_data, local_count_data = local_results
  
      if pred_results is not None:
          local_sum_data, local_count_data = _add_pred_results(
              pred_results,
              local_results,
              axis=axis,
              alpha=alpha,
              alpha_ignore_na=alpha_ignore_na,
              pred_exponent=pred_exponent,
              alpha_data=alpha_data,
          )
      return local_sum_data / local_count_data
  
imports: from collections import namedtuple
  import numpy
  import pandas
  from ....None import opcodes
  from ....serialization.serializables import BoolField, Float64Field
  from ..aggregation import BaseDataFrameExpandingAgg
label_desc: Use image node_id 2 for calling a global function with example usage: _combine_mean(pred_results, local_results, axis, alpha, alpha_ignore_na, pred_exponent) and returns: unknown, unknown