docstr: Evaluate the runtime and peak memory when the backward execution reaches the current region.
docstr_tok: ["Evaluate","the","runtime","and","peak","memory","when","the","backward","execution","reaches","the","current","region","."]
code: def _eval_bwd_mem_per_region(self, region: Region):
      """
      Evaluate the runtime and peak memory when the backward execution reaches the current region.
      """
  
      if region.r_id + 1 < self.region_num:
          self.bwd_reg_flow[region.r_id] = self.bwd_reg_flow[
              region.r_id + 1
          ]
      else:
          self.bwd_reg_flow[region.r_id] = self.fwd_reg_flow[-1]
      self.bwd_reg_flow[region.r_id, self.reg_buffer_to_free] = False
  
      # free gradients in the buffer
      while len(self.reg_buffer_to_free):
          reg_id = self.reg_buffer_to_free.pop(0)
          self.runtime_mem -= self.region_list[reg_id].param_size
  
      # upload parameters of the current region
      if region.is_syn:
          self.runtime_mem += region.param_size
          self.bwd_reg_flow[region.r_id, region.r_id] = True
  
      # prefetch parameters of the region choiced
      bwd_prefetch_region = region.bwd_prefetch_region
      if bwd_prefetch_region:
          self.runtime_mem += bwd_prefetch_region.param_size
          self.bwd_reg_flow[
              region.r_id, bwd_prefetch_region.r_id
          ] = True
  
      # add the gradient of the parameter
      if region.r_id < region.shared_rid:
          # gradient accumulation is required for shared parameters
          self.runtime_mem += 2.0 * region.param_size
      else:
          self.runtime_mem += region.param_size
  
      for node in region.nodes.__reversed__():
          self.runtime_mem -= calculate_fwd_out(node)
          self.runtime_mem += (
              node.meta["bwd_mem_tmp"] + node.meta["bwd_mem_out"]
          )
          self.peak_mem = max(self.runtime_mem, self.peak_mem)
  
          # The memory savings of a node may be negative due to parameter prefetch.
          self.total_mem_saving += (
              node.node_info.runtime_bwd_mem - self.runtime_mem
          )
  
          self.bwd_node_mem[node] = self.runtime_mem
  
          self.runtime_mem -= node.meta[
              "bwd_mem_tmp"
          ] + calculate_fwd_tmp(node)
  
          # free bwd_mem_out
          self.bwd_node_deps[node] = len(node.all_input_nodes)
          for user_node in node.users:
              if user_node in self.bwd_node_deps:
                  self.bwd_node_deps[user_node] -= 1
                  if self.bwd_node_deps[user_node] <= 0:
                      self.runtime_mem -= user_node.meta["bwd_mem_out"]
  
          if self.runtime_mem < 0:
              raise ValueError(
                  f"region id: {region.r_id}, node name: {node.name}, "
                  f"runtime_mem: {self.runtime_mem / 1024 ** 2:.3f}MB ---"
                  f"runtime memory computed less than 0, which is miscalculated!"
              )
  
      # release parameters of the region
      if requires_release_p_in_bwd(self.region_list[region.shared_rid]):
          self.runtime_mem -= region.param_size
  
imports: import bisect
  from abc import ABC, abstractmethod
  from collections import OrderedDict
  from typing import Dict, List
  from torch.fx.node import Node
  from .region import Region
  from .util import *
label_desc: Use image node_id 9 for calling the AsynTrainingSimulator obj's underlying member method code with example usage: obj._eval_bwd_mem_per_region(region) without return types