code: def test_flatten_to_single_vector():
      """Test test_flatten_to_single_vector"""
      x = torch.arange(12).view(2, 1, 3, 2)
      flatten_tensor = flatten_to_single_vector(x)
      expected = np.arange(12).reshape(2, 6)
      # expect [[ 0,  1,  2,  3,  4,  5], [ 6,  7,  8,  9, 10, 11]]
      assert torch.Size([2, 6]) == flatten_tensor.size()
      assert expected.shape == flatten_tensor.shape
  
imports: import collections
  import numpy
  import pytest
  import torch
  from torch import tensor
  import torch.nn.functional
  from garage.envs import GymEnv, normalize
  from garage.experiment.deterministic import set_seed
  from garage.torch import as_torch_dict, compute_advantages, flatten_to_single_vector, global_device, pad_to_last, product_of_gaussians, set_gpu_mode, state_dict_to, torch_to_np
  import garage.torch._functions
  from garage.torch.policies import DeterministicMLPPolicy
  from tests.fixtures import TfGraphTestCase
label_desc: Use image node_id 6 for calling a global function with example usage: test_flatten_to_single_vector() without return types