docstr: If the node has parameters that are in the trackd sparse parameter list, find them and reshape to a 2D tensor with channels last
code: def find_sparse_parameters_for_node(cls, node_name):
      """If the node has parameters that are in the trackd sparse parameter list, find them and reshape to a 2D tensor with channels last"""
      node_weight = None
  
      # check the sparse parameters
      for (
          module_name,
          module,
          p_name,
          p,
          mask,
          pruned,
      ) in cls.__sparse_parameters:
          if node_name_matches(node_name, module_name):
              node_weight = torch.zeros_like(p)
              node_weight.copy_(p)
  
      # if we found something, reshape to concatenate along the same dimension
      if node_weight is not None:
          # Need to handle the concat for layers with different R & S
          shape = node_weight.shape
          # 1d-tensor
          if len(shape) == 1:
              node_weight = node_weight.view(1, shape[0])
          # 2d-tensor (K, C)
          elif len(shape) == 2:
              node_weight = node_weight.view(shape[0], shape[1])
          # 3d-tensor (K, C, R)
          elif len(shape) == 3:
              node_weight = (
                  node_weight.permute(0, 2, 1)
                  .contiguous()
                  .view(shape[0] * shape[2], shape[1])
              )
          # 4d-tensor (K, C, R, S)
          elif len(shape) == 4:
              # convs
              node_weight = (
                  node_weight.permute(2, 3, 0, 1)
                  .contiguous()
                  .view(shape[2] * shape[3] * shape[0], shape[1])
              )
  
      return node_weight
  
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 12 for calling the Permutation obj's underlying member method code with example usage: obj.find_sparse_parameters_for_node(cls, node_name) and returns: node_weight