original_docstring: 
      Computes the expected current value of variables based on how we are aggregating gradients.
      
docstring: Computes the expected current value of variables based on how we are aggregating gradients.
docstring_tokens: Computes the expected current value of variables based on how we are aggregating gradients .
code: def compute_expected_variable_value(
      batch_id: int,
      aggregation_frequency: int,
      average_aggregated_gradient: bool,
  ) -> float:
      """
      Computes the expected current value of variables based on how we are aggregating gradients.
      """
      aggregations_completed = math.floor((batch_id + 1) / aggregation_frequency)
      sum_per_aggregation = 0.0
      for _ in range(aggregation_frequency):
          grads_for_batch = 0.0
          for rank in range(hvd.size()):
              grads_for_batch += rank
          if average_aggregated_gradient:
              grads_for_batch /= float(aggregation_frequency)
          sum_per_aggregation += grads_for_batch / float(hvd.size())
  
      return aggregations_completed * sum_per_aggregation
code_tokens: def compute_expected_variable_value ( batch_id : int , aggregation_frequency : int , average_aggregated_gradient : bool , ) -> float : aggregations_completed = math . floor ( ( batch_id + 1 ) / aggregation_frequency ) sum_per_aggregation = 0.0 for _ in range ( aggregation_frequency ) : grads_for_batch = 0.0 for rank in range ( hvd . size ( ) ) : grads_for_batch += rank if average_aggregated_gradient : grads_for_batch /= float ( aggregation_frequency ) sum_per_aggregation += grads_for_batch / float ( hvd . size ( ) ) return aggregations_completed * sum_per_aggregation
short_docstring: Computes the expected current value of variables based on how we are aggregating gradients.
short_docstring_tokens: Computes the expected current value of variables based on how we are aggregating gradients .
comment: """
      Computes the expected current value of variables based on how we are aggregating gradients.
      """