docstr: This function is used to permutation for a node in C dim. (Only need to handle the weight of the node) 
code: def apply_permutation_in_C_dim(
      cls, node_name, permutation_sequence, dryrun
  ):
      """This function is used to permutation for a node in C dim. (Only need to handle the weight of the node)"""
  
      if cls.__verbosity > 1 and dryrun:
          print(
              "[apply_permutation_in_C_dim] Permutation for node: '{:}' in C dim".format(
                  node_name
              )
          )
  
      if len(permutation_sequence) == 0:
          if cls.__verbosity >= 0:
              print(
                  f"ERROR: [apply_permutation_in_C_dim] the permutation sequence for node {node_name} is empty, fail to apply permutation in C dim."
              )
          return False
  
      is_node_in_sparse_parameters = False
      success_permutation = False
      for (
          module_name,
          module,
          p_name,
          p,
          mask,
          pruned,
      ) in cls.__sparse_parameters:
          if node_name_matches(node_name, module_name):
              if cls.__verbosity > 2 and dryrun:
                  print(
                      "[apply_permutation_in_C_dim] find the node: '{:}' '{:}' in cls.__sparse_parameters, succeed to apply permutation in C dim.".format(
                          node_name, p_name
                      )
                  )
              is_node_in_sparse_parameters = True
              permutation_to_apply = permutation_sequence
              if p.shape[1] != len(
                  permutation_sequence
              ):  # assumed to be grouped convolutions or concatenated weights
                  if p.shape[1] % len(permutation_sequence) != 0:
                      return False
  
                  permutation_to_apply = replicate_sequence(
                      permutation_sequence,
                      p.shape[1] // len(permutation_sequence),
                  )
  
              if not dryrun:
                  p.data.copy_(p[:, permutation_to_apply, ...])
                  cls.__params_permuted_in_C.append(
                      node_name + "." + p_name
                  )
  
              success_permutation = True
      if not is_node_in_sparse_parameters:
          # A special case: if the node itself not in sparse_module_names but one of its real_siblings in sparse_module_names, then the node will not do the permutation search, but it may need to apply the offline permutation in C dim according to the searched permutation sequence from its real_siblings in sparse_module_names
          try:
              for (
                  module_name_from_all_parameters,
                  module_from_all_parameters,
                  p_name_from_all_parameters,
                  p_from_all_parameters,
              ) in cls.__all_parameters:
                  if (
                      node_name_matches(
                          node_name, module_name_from_all_parameters
                      )
                      and p_name_from_all_parameters == "weight"
                  ):
                      if cls.__verbosity > 3 and dryrun:
                          print(
                              "[apply_permutation_in_C_dim] cannot find the node: '{:}' '{:}' in cls.__sparse_parameters, but can find in cls.__all_parameters.".format(
                                  node_name, p_name_from_all_parameters
                              )
                          )
                      permutation_to_apply = permutation_sequence
                      if p_from_all_parameters.shape[1] != len(
                          permutation_sequence
                      ):  # assumed to be grouped convolutions
                          if (
                              p_from_all_parameters.shpae[1]
                              % len(permutation_sequence)
                              != 0
                          ):
                              return False
  
                          permutation_to_apply = replicate_sequence(
                              permutation_sequence,
                              p_from_all_parameters.shape[1]
                              // len(permutation_sequence),
                          )
  
                      if not dryrun:
                          p_from_all_parameters.data.copy_(
                              p_from_all_parameters[
                                  :, permutation_to_apply, ...
                              ]
                          )
                          cls.__params_permuted_in_C.append(
                              node_name
                              + "."
                              + p_name_from_all_parameters
                          )
  
                      success_permutation = True
                      if cls.__verbosity > 2 and dryrun:
                          print(
                              "[apply_permutation_in_C_dim] cannot find the node: '{:}' in cls.__sparse_parameters, after trying with cls.__all_parameters, succeed to apply permutation in C dim.".format(
                                  node_name
                              )
                          )
          except:
              success_permutation = False
              if cls.__verbosity >= 0:
                  print(
                      "ERROR: [apply_permutation_in_C_dim] cannot find the node: '{:}' in cls.__sparse_parameters, after trying with cls.__all_parameters, still fail to apply permutation in C dim.".format(
                          node_name
                      )
                  )
      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 8 for calling the Permutation obj's underlying member method code with example usage: obj.apply_permutation_in_C_dim(cls, node_name, permutation_sequence, dryrun) and returns: success_permutation, False, False, False