Repository: Farama-Foundation/Metaworld
Issue: [Bug Report] AssertionError: meta_batch_size must be divisible by envs_per_task
Root cause: Validation Gap

### Describe the bug

I am trying to train metaworld benchmark data using PPO.
I got following error loading the env.
I have attached the code and error.

Error:
```
(train) root@rjg-3:/home/train# python train.py
Starting training...
Making train environment...
Traceback (most recent call last):
  File "/home/train/train.py", line 10, in <module>
    train_envs = gym.make_vec('Meta-World/ML45-train', vector_strategy='async', seed=seed, meta_batch_size=10, envs_per_task=10)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/train/.venv/lib/python3.11/site-packages/gymnasium/envs/registration.py", line 957, in make_vec
    env = env_creator(num_envs=num_envs, **env_spec_kwargs)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/train/.venv/lib/python3.11/site-packages/metaworld/__init__.py", line 727, in <lambda>
    vector_entry_point=lambda _ml_bench=ml_bench, _split=split, vector_strategy="sync", autoreset_mode=gym.vector.AutoresetMode.SAME_STEP, total_tasks_per_cls=None, seed=None, meta_batch_size=20, num_envs=None, **kwargs: _ml_bench_vector_entry_point(
                                                                                                                                                                                                                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/train/.venv/lib/python3.11/site-packages/metaworld/__init__.py", line 644, in _ml_bench_vector_entry_point
    return env_generator(
           ^^^^^^^^^^^^^^
  File "/home/train/.venv/lib/python3.11/site-packages/metaworld/__init__.py", line 584, in make_ml_envs
    return _make_ml_envs_inner(
           ^^^^^^^^^^^^^^^^^^^^
  File "/home/train/.venv/lib/python3.11/site-packages/metaworld/__init__.py", line 531, in _make_ml_envs_inner
    meta_batch_size % len(all_classes) == 0
AssertionError: meta_batch_size must be divisible by envs_per_task
```
Any advice would be helpful.

### Code example

```shell
Code:

import gymnasium as gym
import metaworld
from stable_baselines3.ppo.policies import MlpPolicy
from stable_baselines3 import PPO
from stable_baselines3.common.evaluation import evaluate_policy

seed = 42
print("Starting training...")
print("Making train environment...")
train_envs = gym.make_vec('Meta-World/ML45-train', vector_strategy='async', seed=seed, meta_batch_size=10, envs_per_task=10)
test_envs = gym.make_vec('Meta-World/ML45-test', vector_strategy='async', seed=seed, meta_batch_size=10, envs_per_task=10)

print("Making model...")
model = PPO(MlpPolicy, train_envs, verbose=0)
print("Training model...")
model.learn(total_timesteps=10_000)
print("Training complete.")
# Evaluate the trained agent
print("Evaluating model...")
mean_reward, std_reward = evaluate_policy(model, test_envs, n_eval_episodes=100)
print("Evaluation complete.")

print(f"mean_reward:{mean_reward:.2f} +/- {std_reward:.2f}")
```

### System info

Ubtuntu

### Additional context

_No response_

### Checklist

- [x] I have checked that there is no similar [issue](https://github.com/Farama-Foundation/Gymnasium/issues) in the repo