code: def test_BufferDict(self):
      buffers = OrderedDict(
          [
              ("b1", torch.randn(10, 10)),
              ("b2", torch.randn(10, 10)),
              ("b3", torch.randn(10, 10)),
          ]
      )
  
      buffer_dict = BufferDict(buffers)
  
      def check():
          self.assertEqual(len(buffer_dict), len(buffers))
          for k1, m2 in zip(buffers, buffer_dict.buffers()):
              self.assertIs(buffers[k1], m2)
          for k1, k2 in zip(buffers, buffer_dict):
              self.assertIs(buffers[k1], buffer_dict[k2])
          for k in buffer_dict:
              self.assertIs(buffer_dict[k], buffers[k])
          for k in buffer_dict.keys():
              self.assertIs(buffer_dict[k], buffers[k])
          for k, v in buffer_dict.items():
              self.assertIs(v, buffers[k])
          for k1, m2 in zip(buffers, buffer_dict.values()):
              self.assertIs(buffers[k1], m2)
          for k in buffers.keys():
              self.assertTrue(k in buffer_dict)
  
      check()
  
      buffers["b4"] = torch.randn(10, 10)
      buffer_dict["b4"] = buffers["b4"]
      check()
  
      next_buffers = [
          ("b5", torch.randn(10, 10)),
          ("b2", torch.randn(10, 10)),
      ]
      buffers.update(next_buffers)
      buffer_dict.update(next_buffers)
      check()
  
      next_buffers = OrderedDict(
          [("b6", torch.randn(10, 10)), ("b5", torch.randn(10, 10))]
      )
      buffers.update(next_buffers)
      buffer_dict.update(next_buffers)
      check()
  
      next_buffers = {
          "b8": torch.randn(10, 10),
          "b7": torch.randn(10, 10),
      }
      buffers.update(sorted(next_buffers.items()))
      buffer_dict.update(next_buffers)
      check()
  
      del buffer_dict["b3"]
      del buffers["b3"]
      check()
  
      with self.assertRaises(TypeError):
          buffer_dict.update(1)
  
      with self.assertRaises(TypeError):
          buffer_dict.update([1])
  
      with self.assertRaises(ValueError):
          buffer_dict.update(torch.randn(10, 10))
  
      with self.assertRaises(TypeError):
          buffer_dict[1] = torch.randn(10, 10)
  
      p_pop = buffer_dict.pop("b4")
      self.assertIs(p_pop, buffers["b4"])
      buffers.pop("b4")
      check()
  
      buffer_dict.clear()
      self.assertEqual(len(buffer_dict), 0)
      buffers.clear()
      check()
  
      # test extra repr
      buffer_dict = BufferDict(
          OrderedDict(
              [
                  ("b1", torch.randn(10, 10)),
                  ("b2", torch.randn(10, 10)),
                  ("b3", torch.randn(10, 10)),
              ]
          )
      )
      self.assertEqual(
          buffer_dict.extra_repr(),
          "  (b1): Buffer containing: [torch.FloatTensor of size 10x10]\n"
          "  (b2): Buffer containing: [torch.FloatTensor of size 10x10]\n"
          "  (b3): Buffer containing: [torch.FloatTensor of size 10x10]",
      )
      # test that calling a buffer dict raises an exception
      with self.assertRaises(RuntimeError):
          buffer_dict(1)
  
file_path: botorch/test/utils/test_torch.py
imports: from __future__ import annotations
  from collections import OrderedDict
  import torch
  from botorch.utils.testing import BotorchTestCase
  from botorch.utils.torch import BufferDict
label_desc: Use image node_id 1 for calling the TestBufferDict obj's underlying member method code with example usage: obj.test_BufferDict() without return types