code: def params(prefix=""):
      weight_0 = torch.tensor(
          [[0.02, 0.03], [0.05, 0.07], [0.11, 0.13]],
          dtype=torch.float32,
      )
      bias_0 = torch.tensor([0.17, 0.19, 0.23], dtype=torch.float32)
      weight_2 = torch.tensor([[0.29, 0.31, 0.37]], dtype=torch.float32)
  
      return {
          f"{prefix}0.weight": weight_0,
          f"{prefix}0.bias": bias_0,
          f"{prefix}2.weight": weight_2,
      }
  
file_path: pytorch-meta/torchmeta/tests/modules/test_parallel.py
imports: import pytest
  import torch
  import torch.nn
  from torchmeta.modules import MetaLinear, MetaSequential
  from torchmeta.modules import DataParallel
  from torchmeta.utils.gradient_based import gradient_update_parameters
label_desc: Use image node_id 3 for calling a global function with example usage: params(prefix) and returns: dict