Running error for all mini-batch runs within examples/pytorch/gcmc

## 🐛 Bug

Mini-batch sampling pipline update, one of the major features released in v0.8, has not been propagated to examples/pytorch/gcmc/train_sampling.py.

## To Reproduce

Step(s) to reproduce the behavior:

(single GPU run)
1. python3 train_sampling.py --data_name=ml-100k --use_one_hot_fea --gcn_agg_accum=stack --gpu 0

Traceback (most recent call last):
File "/workspace/examples/gcmc/train_sampling.py", line 377, in
run(0, n_gpus, args, devices, dataset)
File "/workspace/examples/gcmc/train_sampling.py", line 202, in run
sampler = dgl.dataloading.MultiLayerNeighborSampler([None], return_eids=True)
TypeError: __init__() got an unexpected keyword argument 'return_eids'

## Expected behavior

The case should run through (with single/multi-GPU settings)

## Environment

- DGL Version (e.g., 1.0): 0.9
- Backend Library & Version (e.g., PyTorch 0.4.1, MXNet/Gluon 1.3): 1.12
- OS (e.g., Linux): Ubuntu 20.04
- How you installed DGL (`conda`, `pip`, source): source
- Build command you used (if compiling from source):
- Python version: 3.8.13
- CUDA/cuDNN version (if applicable): 11.7
- GPU models and configuration (e.g. V100): A100
- Any other relevant information:
torchtext 0.13.0

## Additional context

The same type of error (mini-batch sampling update) is also happened to **all TGN examples** (examples/pytorch/tgn/train.py), where EdgeCollator is no longer a valid class name from latest dgl-v0.8x release...