code: def compute_accuracy(net, features, labels, title=None):
      input_tensor = torch.from_numpy(features).type(torch.FloatTensor)
      out_probs = net(input_tensor).detach().numpy()
      out_classes = np.argmax(out_probs, axis=1)
      mlflow.log_metric(
          title, float(sum(out_classes == labels) / len(labels))
      )
      print(title, sum(out_classes == labels) / len(labels))
      return input_tensor
  
imports: import os
  from argparse import ArgumentParser
  import matplotlib.pyplot
  import numpy
  import pandas
  import torch
  from captum.attr import IntegratedGradients, LayerConductance, NeuronConductance
  from prettytable import PrettyTable
  from scipy import stats
  from sklearn.model_selection import train_test_split
  from torch import nn
  import mlflow
label_desc: Use image node_id 6 for calling a global function with example usage: compute_accuracy(net, features, labels, title) and returns: input_tensor