docstr: Handle grouped convolutions to make dimensions match
code: def init_grouped_conv_permutation_flags(
      fx_graph, node_name, node_groups, verbosity
  ):
      """Handle grouped convolutions to make dimensions match"""
  
      node_C = int(fx_graph.get(node_name).get("C_param"))
      node_K = int(fx_graph.get(node_name).get("K_param"))
      node_groups = int(node_groups)
  
      if verbosity > 2:
          print(
              f"\t{node_name} pre-divide C: {node_C}, K: {node_K}, G: {node_groups}"
          )
      assert node_C % node_groups == 0
      node_C = int(node_C / node_groups)
      fx_graph[node_name]["C_param"] = str(node_C)
      if verbosity > 2:
          print(
              f"\t{node_name} post-divide C: {node_C}, K: {node_K}, G: {node_groups}"
          )
  
      if node_C == 1:  # G == C (C is pre-divided by G)
          if (
              node_groups == node_K
          ):  # true depthwise, G == C == K (C will be pre-divided by G)
              fx_graph[node_name]["K_permutable"] = True
              fx_graph[node_name]["K_permuted"] = False
              fx_graph[node_name]["K_passthru"] = True
              fx_graph[node_name]["is_real"] = False
          # else:                                          # G != K, handling a permutation along K would be very tricky and not likely useful
  
      else:  # G != C
          if (
              node_C > 4 and node_C % 4 == 0
          ):  # permutations only help if there's more than one 2:4 pruning group
              fx_graph[node_name]["C_permutable"] = True
              fx_graph[node_name]["C_permuted"] = False
  
imports: import os
  import torch
  import json
  import string
  import time
  import numpy
  import sys
  import builtins
  import io
label_desc: Use image node_id 25 for calling the Permutation obj's underlying member method code with example usage: obj.init_grouped_conv_permutation_flags(fx_graph, node_name, node_groups, verbosity) without return types