code: def set_block_weights_in_torch(weights, torch_block, hidden_size):
      # layernorm 1
      layer_norm_1 = weights[0][0][0]
      layer_norm_1_weight = np.asarray(layer_norm_1[0])
      layer_norm_1_bias = np.asarray(layer_norm_1[1])
      set_param(
          torch_block.attention.layer_norm,
          torch.tensor(layer_norm_1_weight),
          torch.tensor(layer_norm_1_bias),
      )
  
      # lsh weights + output
      attn_weights = weights[0][1]
      if len(attn_weights) < 4:
          set_layer_weights_in_torch_lsh(
              attn_weights, torch_block.attention, hidden_size
          )
      else:
          set_layer_weights_in_torch_local(
              attn_weights, torch_block.attention, hidden_size
          )
  
      # intermediate weighs
      intermediate_weights = weights[2][0][1][2]
  
      # Chunked Feed Forward
      if len(intermediate_weights) == 4:
          intermediate_weights = intermediate_weights[2]
  
      # layernorm 2
      layer_norm_2_weight = np.asarray(intermediate_weights[0][0])
      layer_norm_2_bias = np.asarray(intermediate_weights[0][1])
      set_param(
          torch_block.feed_forward.layer_norm,
          torch.tensor(layer_norm_2_weight),
          torch.tensor(layer_norm_2_bias),
      )
  
      # intermediate dense
      inter_dense_weight = np.asarray(intermediate_weights[1][0])
      inter_dense_bias = np.asarray(intermediate_weights[1][1])
      set_param(
          torch_block.feed_forward.dense.dense,
          torch.tensor(inter_dense_weight).transpose(0, 1).contiguous(),
          torch.tensor(inter_dense_bias),
      )
  
      # intermediate out
      out_dense_weight = np.asarray(intermediate_weights[4][0])
      out_dense_bias = np.asarray(intermediate_weights[4][1])
      set_param(
          torch_block.feed_forward.output.dense,
          torch.tensor(out_dense_weight).transpose(0, 1).contiguous(),
          torch.tensor(out_dense_bias),
      )
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/models/reformer/convert_reformer_trax_checkpoint_to_pytorch.py
imports: import argparse
  import pickle
  import numpy
  import torch
  from torch import nn
  from transformers import ReformerConfig, ReformerModelWithLMHead
  from transformers.utils import logging
label_desc: Use image node_id 4 for calling a global function with example usage: set_block_weights_in_torch(weights, torch_block, hidden_size) without return types