ArrowInvalid: Can only convert 1-dimensional array values
Hi @NielsRogge, thanks for reporting!

In `datasets`, we were handling N-dimensional arrays only when passed as an instance of `np.array`, not when passed as a list of `np.array`s.

I'm fixing it.
## Describe the bug

For the ViT and LayoutLMv2 demo notebooks in my [Transformers-Tutorials repo](https://github.com/NielsRogge/Transformers-Tutorials), people reported an ArrowInvalid issue after applying the following function to a Dataset:

```
def preprocess_data(examples):
  images = [Image.open(path).convert("RGB") for path in examples['image_path']]
  words = examples['words']
  boxes = examples['bboxes']
  word_labels = examples['ner_tags']
  
  encoded_inputs = processor(images, words, boxes=boxes, word_labels=word_labels,
                             padding="max_length", truncation=True)
  
  return encoded_inputs 
```

```
Full trace:

---------------------------------------------------------------------------
ArrowInvalid                              Traceback (most recent call last)
<ipython-input-8-0fc3efc6f0c2> in <module>()
     27 
     28 train_dataset = datasets['train'].map(preprocess_data, batched=True, remove_columns=datasets['train'].column_names,
---> 29                                       features=features)
     30 test_dataset = datasets['test'].map(preprocess_data, batched=True, remove_columns=datasets['test'].column_names,
     31                                       features=features)

13 frames
/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)
   1701                 new_fingerprint=new_fingerprint,
   1702                 disable_tqdm=disable_tqdm,
-> 1703                 desc=desc,
   1704             )
   1705         else:

/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs)
    183         }
    184         # apply actual function
--> 185         out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
    186         datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
    187         # re-apply format to the output

/usr/local/lib/python3.7/dist-packages/datasets/fingerprint.py in wrapper(*args, **kwargs)
    396             # Call actual function
    397 
--> 398             out = func(self, *args, **kwargs)
    399 
    400             # Update fingerprint of in-place transforms + update in-place history of transforms

/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, disable_tqdm, desc, cache_only)
   2063                                 writer.write_table(batch)
   2064                             else:
-> 2065                                 writer.write_batch(batch)
   2066                 if update_data and writer is not None:
   2067                     writer.finalize()  # close_stream=bool(buf_writer is None))  # We only close if we are writing in a file

/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size)
    409             typed_sequence = OptimizedTypedSequence(batch_examples[col], type=col_type, try_type=col_try_type, col=col)
    410             typed_sequence_examples[col] = typed_sequence
--> 411         pa_table = pa.Table.from_pydict(typed_sequence_examples)
    412         self.write_table(pa_table, writer_batch_size)
    413 

/usr/local/lib/python3.7/dist-packages/pyarrow/table.pxi in pyarrow.lib.Table.from_pydict()

/usr/local/lib/python3.7/dist-packages/pyarrow/array.pxi in pyarrow.lib.asarray()

/usr/local/lib/python3.7/dist-packages/pyarrow/array.pxi in pyarrow.lib.array()

/usr/local/lib/python3.7/dist-packages/pyarrow/array.pxi in pyarrow.lib._handle_arrow_array_protocol()

/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py in __arrow_array__(self, type)
    106                     storage = numpy_to_pyarrow_listarray(self.data, type=type.value_type)
    107                 else:
--> 108                     storage = pa.array(self.data, type.storage_dtype)
    109                 out = pa.ExtensionArray.from_storage(type, storage)
    110             elif isinstance(self.data, np.ndarray):

/usr/local/lib/python3.7/dist-packages/pyarrow/array.pxi in pyarrow.lib.array()

/usr/local/lib/python3.7/dist-packages/pyarrow/array.pxi in pyarrow.lib._sequence_to_array()

/usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status()

/usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.check_status()

ArrowInvalid: Can only convert 1-dimensional array values
```
It can be fixed by adding the following line:

```diff
def preprocess_data(examples):
  images = [Image.open(path).convert("RGB") for path in examples['image_path']]
  words = examples['words']
  boxes = examples['bboxes']
  word_labels = examples['ner_tags']
  
  encoded_inputs = processor(images, words, boxes=boxes, word_labels=word_labels,
                             padding="max_length", truncation=True)
+ encoded_inputs["image"] = np.array(encoded_inputs["image"])
  
  return encoded_inputs 
```

However, would be great if this can be fixed within Datasets itself.