code: def setUp(self):
      # [0, 1, 1, 1, 3, 1]
      self.predictions = torch.Tensor(
          [
              [0.35, 0.25, 0.1, 0.1, 0.2],
              [0.1, 0.6, 0.1, 0.2, 0.0],
              [0.1, 0.6, 0.1, 0.2, 0.0],
              [0.1, 0.5, 0.1, 0.2, 0.0],
              [0.1, 0.2, 0.1, 0.7, 0.0],
              [0.1, 0.6, 0.1, 0.2, 0.0],
          ]
      )
      self.targets = torch.Tensor([0, 4, 1, 0, 3, 0])
  
      # detailed target state
      self.pred_sum = [1, 4, 0, 1, 0]
      self.true_sum = [3, 1, 0, 1, 1]
      self.true_positive_sum = [1, 1, 0, 1, 0]
      self.true_negative_sum = [3, 2, 6, 5, 5]
      self.total_sum = [6, 6, 6, 6, 6]
  
      desired_precisions = [1.00, 0.25, 0.00, 1.00, 0.00]
      desired_recalls = [0.33, 1.00, 0.00, 1.00, 0.00]
      desired_fscores = [
          (2 * p * r) / (p + r) if p + r != 0.0 else 0.0
          for p, r in zip(desired_precisions, desired_recalls)
      ]
      self.desired_precisions = desired_precisions
      self.desired_recalls = desired_recalls
      self.desired_fscores = desired_fscores
  
imports: from unittest import TestCase
  import numpy
  import torch
  import torch.nn
  from poutyne import F1, BinaryF1, FBeta, Model
label_desc: Use image node_id 1 for calling the BaseFBetaTest obj's underlying member method code with example usage: obj.setUp() without return types