code: def test_entropy_for_uniform_distribution(self, device: str):
      metric = Entropy()
      logits = torch.tensor(
          [[1, 1, 1, 1], [1, 1, 1, 1]], dtype=torch.float, device=device
      )
      metric(logits)
      assert_allclose(metric.get_metric()["entropy"], 1.38629436)
      # actual values shouldn't effect uniform distribution:
      logits = torch.tensor(
          [[2, 2, 2, 2], [2, 2, 2, 2]], dtype=torch.float, device=device
      )
      metric(logits)
      assert_allclose(metric.get_metric()["entropy"], 1.38629436)
  
      metric.reset()
      assert metric._entropy == 0.0
      assert metric._count == 0.0
  
file_path: allennlp/tests/training/metrics/entropy_test.py
imports: from typing import Any, Dict, List, Tuple, Union
  import torch
  from torch.testing import assert_allclose
  from allennlp.common.testing import AllenNlpTestCase, multi_device, global_distributed_metric, run_distributed_test
  from allennlp.training.metrics import Entropy
label_desc: Use image node_id 2 for calling the EntropyTest obj's underlying member method code with example usage: obj.test_entropy_for_uniform_distribution(device) without return types