code: def setUp(self):
      # [0, 1, 1, 1, 0, 1]
      self.predictions = torch.Tensor(
          [
              [0.35, 0.25],
              [0.1, 0.6],
              [0.1, 0.6],
              [0.1, 0.5],
              [0.2, 0.1],
              [0.1, 0.6],
          ]
      )
      self.targets = torch.Tensor([0, 0, 1, 0, 1, 0])
  
      # detailed target state
      self.pred_sum = [2, 4]
      self.true_sum = [4, 2]
      self.true_positive_sum = [1, 1]
      self.true_negative_sum = [1, 1]
      self.total_sum = [6, 6]
  
      desired_precision = 0.25
      desired_recall = 0.5
      desired_fscore = (
          (2 * desired_precision * desired_recall)
          / (desired_precision + desired_recall)
          if desired_precision + desired_recall != 0.0
          else 0.0
      )
      self.output = [desired_fscore, desired_precision, desired_recall]
  
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 BaseFBetaBinaryTest obj's underlying member method code with example usage: obj.setUp() without return types