Tensor operation not functioning in dataset mapping
Hi! The Torch ops in your snippet are not equivalent to the NumPy ones, hence the difference. You can get the same behavior by replacing the line `feature = torch.mean(feature, dim=1)` with `feature = feature.squeeze().mean(1)` . > Hi! The Torch ops in your snippet are not equivalent to the NumPy ones, hence the difference. You can get the same behavior by replacing the line `feature = torch.mean(feature, dim=1)` with `feature = feature.squeeze().mean(1)` .

Thank you. 
## Describe the bug
I'm doing a torch.mean() operation in data preprocessing, and it's not working.

## Steps to reproduce the bug
```
from transformers import pipeline
import torch
import numpy as np
from datasets import load_dataset
device = 'cuda:0'
raw_dataset = load_dataset("glue", "sst2")
feature_extraction = pipeline('feature-extraction', 'bert-base-uncased', device=device)
def extracted_data(examples):
    # feature = torch.tensor(feature_extraction(examples['sentence'], batch_size=16), device=device)
    # feature = torch.mean(feature, dim=1)
    feature = np.asarray(feature_extraction(examples['sentence'], batch_size=16)).squeeze().mean(1)
    print(feature.shape)
    return {'feature': feature}

extracted_dataset = raw_dataset.map(extracted_data, batched=True, batch_size=16)
```

## Results
When running with torch.mean(), the shape printed out is [16, seq_len, 768], which is exactly the same before the operation. While numpy works just fine, which gives [16, 768].


## Environment info
- `datasets` version: 2.6.1
- Platform: Linux-4.4.0-142-generic-x86_64-with-glibc2.31
- Python version: 3.10.6
- PyArrow version: 9.0.0
- Pandas version: 1.5.0
