code: def test_fbeta_handles_batch_size_of_one(self):
      predictions = torch.Tensor([[0.2862, 0.3479, 0.1627, 0.2033]])
      targets = torch.Tensor([1])
      mask = torch.Tensor([1])
  
      fbeta = FBeta(make_deterministic=self.make_deterministic)
      fbeta.update(predictions, (targets, mask))
  
      numpy.testing.assert_almost_equal(
          fbeta._pred_sum.tolist(), [0.0, 1.0, 0.0, 0.0]
      )
      numpy.testing.assert_almost_equal(
          fbeta._true_sum.tolist(), [0.0, 1.0, 0.0, 0.0]
      )
      numpy.testing.assert_almost_equal(
          fbeta._true_positive_sum.tolist(), [0.0, 1.0, 0.0, 0.0]
      )
      numpy.testing.assert_almost_equal(
          fbeta._total_sum.tolist(), [1.0, 1.0, 1.0, 1.0]
      )
  
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 13 for calling the BaseFBetaTest obj's underlying member method code with example usage: obj.test_fbeta_handles_batch_size_of_one() without return types