code: def find_most_h_w(all_layer_matrices):
      shape_info = torch.vstack(
          [
              torch.as_tensor(
                  [
                      layer_matrix.shape[-2]
                      for layer_matrix in all_layer_matrices
                  ]
              ),
              torch.as_tensor(
                  [
                      layer_matrix.shape[-1]
                      for layer_matrix in all_layer_matrices
                  ]
              ),
          ]
      )
      return (
          torch.mode(shape_info).values[0],
          torch.mode(shape_info).values[1],
      )
  
imports: import math
  from functools import partial
  from typing import Tuple, List
  import numpy
  import torch
  from torch import nn
  from torch.nn.init import trunc_normal_
  from einops import rearrange
  from towhee.models.layers.layers_with_relprop import GELU, Linear, Dropout, Conv2d, Conv3d, Softmax, Sigmoid, LayerNorm, Einsum, Add, Clone, MaxPool3d, IndexSelect
label_desc: Use image node_id 1 for calling a global function with example usage: find_most_h_w(all_layer_matrices) and returns: torch,  torch