text: Title: AddMetaPaths may cause memory leak issue
  Body: ### 🐛 Describe the bug
  
  Hi! I am studying the adversarial attack on heterogeneous graph neural networks (e.g. HAN). 
  Normally, we call the AddMetaPaths function once to train HAN.
  
  ```
  import torch_geometric.transforms as T
  from torch_geometric.datasets import DBLP
  path = osp.join(osp.dirname(osp.realpath(__file__)), './data/DBLP')
  # APA, APCPA, APTPA
  metapaths = [[('author', 'paper'), ('paper', 'author')],
               [('author', 'paper'), ('paper', 'conference'), ('conference', 'paper'), ('paper', 'author')],
               [('author', 'paper'), ('paper', 'term'), ('term', 'paper'), ('paper', 'author')]]
  transform = T.AddMetaPaths(metapaths=metapaths, drop_orig_edge_types=True, drop_unconnected_node_types=True)
  dataset = DBLP(path, transform=transform)
  data = dataset[0]
  ```
  
  The training process and model are similar to the example in examples/hetero/han_imdb.py, and it is no problem.
  Then, I want to change the graph and perform targeted attacks on HAN. Hence, we need to call AddMetaPaths many times.
  
  ```
  for id in target_ids:
      mod_data = attack(ori_data)
      transform = T.AddMetaPaths(metapaths=metapaths, drop_orig_edge_types=True, drop_unconnected_node_types=True)
      new_metadata = transform(mod_data)
      gnn(new_metadata.x_dict, new_metadata.edge_index_dict)
      eval()
  ```
  
  The procedure will be killed. I use top and memory_profiler and find that as the number of iterations increases, `new_metadata = transform(mod_data)` will consume a significant amount of memory space. I tried `del mod_data` or `del new_metadata`  at the end of the iteration, but the problem still exists. The following code can be used to reproduce the issue without attack
  
  ```
  import torch_geometric.transforms as T
  from torch_geometric.datasets import DBLP
  path = osp.join(osp.dirname(osp.realpath(__file__)), './data/DBLP')
  # APA, APCPA, APTPA
  metapaths = [[('author', 'paper'), ('paper', 'author')],
               [('author', 'paper'), ('paper', 'conference'), ('conference', 'paper'), ('paper', 'author')],
               [('author', 'paper'), ('paper', 'term'), ('term', 'paper'), ('paper', 'author')]]
  transform = T.AddMetaPaths(metapaths=metapaths, drop_orig_edge_types=True, drop_unconnected_node_types=True)
  dataset = DBLP(path, transform=None)
  oridata = dataset[0]
  
  for idx in range(2000):
      data = transform(oridata)
  
  ```
  
  It seems that `mod_data` will not cause the problem. If I need to call AddMetaPaths in each iteration, What is the right way to avoid memory leak? The environment is torch_geometric-2.5.2, torch-2.1.2.
  
  
  ### Versions
  
  Collecting environment information...
  PyTorch version: 2.1.2
  Is debug build: False
  CUDA used to build PyTorch: 11.8
  ROCM used to build PyTorch: N/A
  
  OS: Ubuntu 20.04.1 LTS (x86_64)
  GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
  Clang version: Could not collect
  CMake version: Could not collect
  Libc version: glibc-2.31
  
  Python version: 3.8.19 (default, Mar 20 2024, 19:58:24)  [GCC 11.2.0] (64-bit runtime)
  Python platform: Linux-5.4.0-42-generic-x86_64-with-glibc2.17
  Is CUDA available: True
  CUDA runtime version: 12.0.76
  CUDA_MODULE_LOADING set to: LAZY
  GPU models and configuration: 
  GPU 0: NVIDIA GeForce RTX 3090
  GPU 1: NVIDIA GeForce RTX 3090
  GPU 2: NVIDIA GeForce RTX 3090
  GPU 3: NVIDIA GeForce RTX 3090
  
  Nvidia driver version: 525.125.06
  cuDNN version: Probably one of the following:
  /usr/local/cuda-12.0/targets/x86_64-linux/lib/libcudnn.so.8
  /usr/local/cuda-12.0/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8
  /usr/local/cuda-12.0/targets/x86_64-linux/lib/libcudnn_adv_train.so.8
  /usr/local/cuda-12.0/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8
  /usr/local/cuda-12.0/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8
  /usr/local/cuda-12.0/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8
  /usr/local/cuda-12.0/targets/x86_64-linux/lib/libcudnn_ops_train.so.8
  HIP runtime version: N/A
  MIOpen runtime version: N/A
  Is XNNPACK available: True
  
  CPU:
  Architecture:                    x86_64
  CPU op-mode(s):                  32-bit, 64-bit
  Byte Order:                      Little Endian
  Address sizes:                   46 bits physical, 57 bits virtual
  CPU(s):                          48
  On-line CPU(s) list:             0-47
  Thread(s) per core:              2
  Core(s) per socket:              12
  Socket(s):                       2
  NUMA node(s):                    2
  Vendor ID:                       GenuineIntel
  CPU family:                      6
  Model:                           106
  Model name:                      Intel(R) Xeon(R) Silver 4310 CPU @ 2.10GHz
  Stepping:                        6
  Frequency boost:                 enabled
  CPU MHz:                         877.503
  CPU max MHz:                     3300.0000
  CPU min MHz:                     800.0000
  BogoMIPS:                        4200.00
  Virtualization:                  VT-x
  L1d cache:                       1.1 MiB
  L1i cache:                       768 KiB
  L2 cache:                        30 MiB
  L3 cache:                        36 MiB
  NUMA node0 CPU(s):               0-11,24-35
  NUMA node1 CPU(s):               12-23,36-47
  Vulnerability Itlb multihit:     Not affected
  Vulnerability L1tf:              Not affected
  Vulnerability Mds:               Not affected
  Vulnerability Meltdown:          Not affected
  Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
  Vulnerability Spectre v1:        Mitigation; usercopy/swapgs barriers and __user pointer sanitization
  Vulnerability Spectre v2:        Mitigation; Enhanced IBRS, IBPB conditional, RSB filling
  Vulnerability Srbds:             Not affected
  Vulnerability Tsx async abort:   Not affected
  Flags:                           fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 invpcid_single ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local wbnoinvd dtherm ida arat pln pts avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq rdpid md_clear pconfig flush_l1d arch_capabilities
  
  Versions of relevant libraries:
  [pip3] numpy==1.24.3
  [pip3] torch==2.1.2
  [pip3] torch-cluster==1.6.3+pt21cu118
  [pip3] torch_geometric==2.5.2
  [pip3] torch-scatter==2.1.2+pt21cu118
  [pip3] torch-sparse==0.6.18+pt21cu118
  [pip3] torch-spline-conv==1.2.2+pt21cu118
  [pip3] torchaudio==2.1.2
  [pip3] torchvision==0.16.2
  [pip3] triton==2.1.0
  [conda] blas                      1.0                         mkl  
  [conda] ffmpeg                    4.3                  hf484d3e_0    pytorch
  [conda] libjpeg-turbo             2.0.0                h9bf148f_0    pytorch
  [conda] mkl                       2023.1.0         h213fc3f_46344  
  [conda] mkl-service               2.4.0            py38h5eee18b_1  
  [conda] mkl_fft                   1.3.8            py38h5eee18b_0  
  [conda] mkl_random                1.2.4            py38hdb19cb5_0  
  [conda] numpy                     1.24.3           py38hf6e8229_1  
  [conda] numpy-base                1.24.3           py38h060ed82_1  
  [conda] pyg                       2.5.2           py38_torch_2.1.0_cu118    pyg
  [conda] pytorch                   2.1.2           py3.8_cuda11.8_cudnn8.7.0_0    pytorch
  [conda] pytorch-cuda              11.8                 h7e8668a_5    pytorch
  [conda] pytorch-mutex             1.0                        cuda    pytorch
  [conda] torch-cluster             1.6.3+pt21cu118          pypi_0    pypi
  [conda] torch-scatter             2.1.2+pt21cu118          pypi_0    pypi
  [conda] torch-sparse              0.6.18+pt21cu118          pypi_0    pypi
  [conda] torch-spline-conv         1.2.2+pt21cu118          pypi_0    pypi
  [conda] torchaudio                2.1.2                py38_cu118    pytorch
  [conda] torchtriton               2.1.0                      py38    pytorch
  [conda] torchvision               0.16.2               py38_cu118    pytorch