KeyError: dataset has no key "image"

### Describe the bug

I've loaded a local image dataset with:
`ds = laod_dataset("imagefolder", data_dir=path-to-data)`

And defined a transform to process the data, following the Datasets docs.

However, I get a keyError error, indicating there's no "image" key in my dataset. When I printed out the example_batch sent to the transformation function, it shows only the labels are being sent to the function.
For some reason, the images are not in the example batches.

### Steps to reproduce the bug

I'm using the latest stable version of datasets 

### Expected behavior

I expect the example_batches to contain both images and labels

### Environment info

I'm using the latest stable version of datasets 

You can list the dataset's columns with `ds.column_names` before `.map` to check whether the dataset has an `image` column. If it doesn't, then this is a bug. Otherwise, please paste the line with the `.map` call.




This is the piece of code I am running:
```
data_transforms = utils.get_data_augmentation(args)
image_dataset = utils.load_image_dataset(args.dataset)

def resize(examples):
    examples["pixel_values"] = [image.convert("RGB").resize((300, 300)) for image in examples["image"]]
    return examples

def preprocess_train(example_batch):
    print(f"Example batch: \n{example_batch}")
    example_batch["pixel_values"] = [
        data_transforms["train"](image.convert("RGB")) for image in example_batch["pixel_values"]
    ]
    return example_batch

def preprocess_val(example_batch):
    example_batch["pixel_values"] = [
        data_transforms["val"](image.convert("RGB")) for image in example_batch["pixel_values"]
    ]
    return example_batch

image_dataset = image_dataset.map(resize, remove_columns=["image"], batched=True)

image_dataset["train"].set_transform(preprocess_train)
image_dataset["validation"].set_transform(preprocess_val)
```

When I print ds.column_names I get the following
`{'train': ['image', 'label'], 'validation': ['image', 'label'], 'test': ['image', 'label']}`

The `print(f"Example batch: \n{example_batch}")` in the `preprocess_train` function outputs only labels without images:
```
Example batch: 
{'label': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]}
```

The weird part of it all is that a sample code runs in a jupyter lab notebook without any bugs, but when I run my scripts from the terminal I get the bug. The same code.

The `remove_columns=["image"]` argument in the `.map` call removes the `image` column from the output, so drop this argument to preserve it.

The problem is not with the removal of the image key. The bug is why only the labels are sent to be process, instead of all the featues or dictionary keys.

P.S. I just dropped the removal argument as you've suggested, but that didn't solve the problem, because only the labels are being sent to be processed

All the `image_dataset.column_names` after the `map` call should also be present in `preprocess_train `/`preprocess_val` unless (input) `columns` in `set_transform` are specified.

If that's not the case, we need a full reproducer (not snippets) with the environment info.

I have resolved the error after including a collate function as indicated in the Quick Start session of the Datasets docs.:

Here is what I did:
```
data_transforms = utils.get_data_augmentation(args)
image_dataset = utils.load_image_dataset(args.dataset)

def preprocess_train(example_batch):
    example_batch["pixel_values"] = [
        data_transforms["train"](image.convert("RGB")) for image in example_batch["image"]
    ]
    return example_batch

def preprocess_val(example_batch):
    example_batch["pixel_values"] = [
        data_transforms["val"](image.convert("RGB")) for image in example_batch["image"]
    ]
    return example_batch

def collate_fn(examples):
    images = []
    labels = []
    for example in examples:
        images.append((example["pixel_values"]))
        labels.append(example["label"])

    pixel_values = torch.stack(images)
    labels = torch.tensor(labels)
    return {"pixel_values": pixel_values, "label": labels}

train_dataset = image_dataset["train"].with_transform(preprocess_train)
val_dataset = image_dataset["validation"].with_transform(preprocess_val)

image_datasets = {
    "train": train_dataset,
    "val": val_dataset
}

samplers = {
    "train": data.RandomSampler(train_dataset),
    "val": data.SequentialSampler(val_dataset),
}

dataloaders = {
    x: data.DataLoader(
        image_datasets[x],
        collate_fn=collate_fn,
        batch_size=batch_size,
        sampler=samplers[x],
        num_workers=args.num_workers,
        worker_init_fn=utils.set_seed_for_worker,
        generator=g,
        pin_memory=True,
    )
    for x in ["train", "val"]
}

train_loader, val_loader = dataloaders["train"], dataloaders["val"]
```
Everything runs fine without any bug now.