docstr: This function is used to permutation for a node in K dim. (Need to handle the weight/bias/running_mean/running_var of the node)
code: def apply_permutation_in_K_dim(
      cls, node_name, permutation_sequence, fx_graph, dryrun
  ):
      """This function is used to permutation for a node in K dim. (Need to handle the weight/bias/running_mean/running_var of the node)"""
  
      if cls.__verbosity > 1:
          print(
              "[apply_permutation_in_K_dim] Permutation for node: '{:}' in K dim".format(
                  node_name
              )
          )
  
      if len(permutation_sequence) == 0:
          if cls.__verbosity >= 0:
              print(
                  "ERROR: [apply_permutation_in_K_dim] the permutation sequence is empty, fail to apply permutation in K dim."
              )
          return False
  
      # permute attribute nodes
      if "attr" in fx_graph[node_name].keys():
          return cls.permute_attr(
              node_name, permutation_sequence, fx_graph, dryrun
          )
  
      # if we didn't store the attribute already, look in the modules' parameters
      is_node_in_all_parameters = False
      success_permutation = False
  
      for module_name, module, p_name, p in cls.__all_parameters:
          if node_name_matches(node_name, module_name):
              if cls.__verbosity > 1 and dryrun:
                  print(
                      "[apply_permutation_in_K_dim] find the node: '{:}' with '{:}' in cls.__all_parameters, may succeed to apply permutation in K dim.".format(
                          node_name, p_name
                      )
                  )
              is_node_in_all_parameters = True
              permutation_to_apply = permutation_sequence
  
              if p.shape[0] != len(
                  permutation_sequence
              ):  # assumed to be grouped convolutions
                  if cls.__verbosity > 2 and dryrun:
                      print(
                          f"Mismatch in K dimension between found module {module_name} {p_name} for node {node_name}: permutation length {len(permutation_sequence)} but parameter shape in K {p.shape[0]}"
                      )
  
                  if p.shape[0] % len(permutation_sequence) != 0:
                      return False
  
                  permutation_to_apply = replicate_sequence(
                      permutation_sequence,
                      p.shape[0] // len(permutation_sequence),
                  )
  
                  if cls.__verbosity > 1 and dryrun:
                      print(
                          "[apply_permutation_in_K_dim] the node: '{:}' with shape: '{:}' required replicating the permutation sequence with len '{:}' {:} times to succeed in applying the permutation in the K dimension.".format(
                              node_name,
                              p.shape,
                              len(permutation_sequence),
                              p.shape[0] // len(permutation_sequence),
                          )
                      )
              else:
                  if cls.__verbosity > 1 and dryrun:
                      print(
                          "[apply_permutation_in_K_dim] the node: '{:}' with shape: '{:}', can match the size of permutation sequence with len: '{:}', succeed to apply permutation in K dim.".format(
                              node_name,
                              p.shape,
                              len(permutation_sequence),
                          )
                      )
  
              if not dryrun:
                  p.data.copy_(p[permutation_to_apply, ...])
                  cls.__params_permuted_in_K.append(
                      node_name + "." + p_name
                  )
  
              success_permutation = True
  
      if not is_node_in_all_parameters:
          if cls.__verbosity >= 0:
              print(
                  "ERROR: [apply_permutation_in _K_dim] cannot find the node: '{:}' in cls.__all_parameters, fail to apply permutation in K dim.".format(
                      node_name
                  )
              )
          success_permutation = False
  
      return success_permutation
  
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 10 for calling the Permutation obj's underlying member method code with example usage: obj.apply_permutation_in_K_dim(cls, node_name, permutation_sequence, fx_graph, dryrun) and returns: success_permutation, False, cls, False