Add num_test_batches option
`to_tf_dataset` calls can be very costly because of the number of test batches drawn during `_get_output_signature`. The test batches are draw in order to estimate the shapes when creating the tensorflow dataset. This is necessary when the shapes can be irregular, but not in cases when the tensor shapes are the same across all samples. This PR adds an option to change the number of batches drawn, so the user can speed this conversion up. 

Running the following, and modifying `num_test_batches`
```
import time
from datasets import load_dataset
from transformers import DefaultDataCollator

data_collator = DefaultDataCollator()
dataset = load_dataset("beans")
dataset = dataset["train"].with_format("np")
start = time.time()
dataset = dataset.to_tf_dataset(
    columns=["image"],
    label_cols=["label"],
    batch_size=8,
    collate_fn=data_collator,
    num_test_batches=NUM_TEST_BATCHES,
)
end = time.time()
print(end - start)
```

NUM_TEST_BATCHES=200:  0.8197s
NUM_TEST_BATCHES=50:    0.3070s
NUM_TEST_BATCHES=2:      0.1417s
NUM_TEST_BATCHES=1:      0.1352s 