text: import numpy as np
  import torch
  import helper
  import os
  
  # General global variable declaration
  device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
  number_of_dofs = 12000
  
  # Create the architecture and load the trained weights
  network = helper.init_network(device=device, name="Beam_LR_STAR", number_of_dofs=number_of_dofs)
  print(network)
  
  # Load input forces
  forces_numpy = np.load(f"{os.path.dirname(os.path.realpath(__file__))}/Input_forces.npy")
  forces = torch.as_tensor(data=forces_numpy, dtype=torch.float, device=device)
  forces.requires_grad = False
  
  # Load corresponding displacements
  ground_truth = np.load(f"{os.path.dirname(os.path.realpath(__file__))}/Displacements.npy")
  
  for i, force in enumerate(forces):
      print(f"\nSample n°{i}:")
  
      prediction = network(force.view((1, -1))).cpu().detach().numpy()
      maximum_relative_l2_error_value = helper.maximum_relative_l2(prediction=prediction, ground_truth=ground_truth[i]) * 100
      mean_relative_l2_error_value = helper.mean_relative_l2(prediction=prediction, ground_truth=ground_truth[i]) * 100
      SNR_value = helper.signal_to_noise_ratio(prediction=prediction, ground_truth=ground_truth[i])
      print(f"\tMaximum relative L2 error  : {maximum_relative_l2_error_value} %")
      print(f"\tMean relative L2 error  : {mean_relative_l2_error_value} %")
      print(f"\tSNR  : {SNR_value}")
  