IterableDataset.from_generator() fails with pickle error when provided a generator or iterator
State: open

### Describe the bug

**Description**
Providing a generator in an instantiation of IterableDataset.from_generator() fails with `TypeError: cannot pickle 'generator' object` when the generator argument is supplied with a generator. 

**Code example**
```
def line_generator(files: List[Path]):

    if isinstance(files, str):
        files = [Path(files)]

    for file in files:
        if isinstance(file, str):
            file = Path(file)
        yield from open(file,'r').readlines()

...
model_training_files = ['file1.txt', 'file2.txt', 'file3.txt']
train_dataset = IterableDataset.from_generator(generator=line_generator(model_training_files))
```

**Traceback**
Traceback (most recent call last):
  File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/contextlib.py", line 135, in __exit__
    self.gen.throw(type, value, traceback)
  File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 691, in _no_cache_fields
    yield
  File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 701, in dumps
    dump(obj, file)
  File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 676, in dump
    Pickler(file, recurse=True).dump(obj)
  File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/dill/_dill.py", line 394, in dump
    StockPickler.dump(self, obj)
  File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/pickle.py", line 487, in dump
    self.save(obj)
  File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 666, in save
    dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
  File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/dill/_dill.py", line 388, in save
    StockPickler.save(self, obj, save_persistent_id)
  File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/pickle.py", line 560, in save
    f(self, obj)  # Call unbound method with explicit self
  File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/dill/_dill.py", line 1186, in save_module_dict
    StockPickler.save_dict(pickler, obj)
  File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/pickle.py", line 971, in save_dict
    self._batch_setitems(obj.items())
  File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/pickle.py", line 997, in _batch_setitems
    save(v)
  File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 666, in save
    dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id)
  File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/dill/_dill.py", line 388, in save
    StockPickler.save(self, obj, save_persistent_id)
  File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/pickle.py", line 578, in save
    rv = reduce(self.proto)
TypeError: cannot pickle 'generator' object


### Steps to reproduce the bug

1. Create a set of text files to iterate over. 
2. Create a generator that returns the lines in each file until all files are exhausted.
3. Instantiate the dataset over the generator by instantiating an IterableDataset.from_generator().
4. Wait for the explosion.

### Expected behavior

I would expect that since the function claims to accept a generator that there would be no crash. Instead, I would expect the dataset to return all the lines in the files as queued up in the `line_generator()` function.

### Environment info

datasets.__version__ == '2.13.1'
Python 3.9.6
Platform: Darwin WE35261 22.5.0 Darwin Kernel Version 22.5.0: Thu Jun  8 22:22:22 PDT 2023; root:xnu-8796.121.3~7/RELEASE_X86_64 x86_64


Comments:
Hi! `IterableDataset.from_generator` expects a generator function, not the object (to be consistent with `Dataset.from_generator`).

You can fix the above snippet as follows:
```python
train_dataset = IterableDataset.from_generator(line_generator, fn_kwargs={"files": model_training_files})
```

to anyone reaching this issue, the argument is `gen_kwargs`:
```py
train_dataset = IterableDataset.from_generator(line_generator, gen_kwargs={"files": model_training_files})
```

This still fails, for both Dataset and IterableDataset

```python
  records = [1, 2, 3]

  gen = ({"row": str(x)} for x in records)

  dataset  = IterableDataset.from_generator(generator=gen)
  ```

Edit: gen_kwargs must be picklable, it can't be an iterator even if you are not doing multiprocessing, the same goes for included namespace variables.

> This still fails, for both Dataset and IterableDataset
> 
>   records = [1, 2, 3]
> 
>   gen = ({"row": str(x)} for x in records)
> 
>   dataset  = IterableDataset.from_generator(generator=gen)
> Edit: gen_kwargs must be picklable, it can't be an iterator even if you are not doing multiprocessing, the same goes for included namespace variables.

@PheelaV I figured out the issue, and it's incredibly dumb.

You don't pass in the generator. You pass in the function that builds the generator.

```
records = [1, 2, 3]

def gen():
    for i in range(100):
        yield {"row": str(i)} )


# this does not work, however this is how you would expect it to work
dataset  = IterableDataset.from_generator(generator=gen())   # pass in a generator


# this does work, however this is NOT how you would expect it to work
dataset  = IterableDataset.from_generator(generator=gen)  # pass in a function that returns a generator once invoked
```