code: def make_params(
      num_params,
      sizes=[1, 2, 3, 4, 5],
      num_dims=[1, 2, 3],
      dtypes=[torch.float32],
      devices=["cuda"],
      make_copy=False,
  ):
      """Construct parameters with random configurations"""
  
      # Construct parameters
      params = []
      for _ in range(num_params):
          dims = [
              random.choice(sizes)
              for _ in range(random.choice(num_dims))
          ]
          dtype = random.choice(dtypes)
          device = random.choice(devices)
          p = torch.nn.Parameter(
              torch.randn(dims, dtype=dtype, device=device)
          )
          p.grad = torch.randn_like(p)
          params.append(p)
  
      # Copy parameters if needed
      if make_copy:
          params_copy = []
          for p in params:
              p_copy = p.clone().detach()
              p_copy.grad = p.grad.clone().detach()
              params_copy.append(p_copy)
          return params, params_copy
      else:
          return params
  
file_path: apex/apex/contrib/test/clip_grad/test_clip_grad.py
imports: import random
  import unittest
  import torch
label_desc: Use image node_id 1 for calling a global function with example usage: make_params(num_params, sizes, num_dims, dtypes, devices, make_copy) and returns: params,  params_copy, params