weird output shape when fine-tuning TFDistilBertForSequenceClassification
I'm trying to fine-tune `TFDistilBertForSequenceClassification` for multi-class classification (100 classes) on a custom dataset following the tutorial at https://huggingface.co/transformers/custom_datasets.html.

I'm following the workflow for fine-tuning in native tensorflow, i.e.: 

```
from transformers import TFDistilBertForSequenceClassification
model = TFDistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased')
optimizer = tf.keras.optimizers.Adam(learning_rate=5e-5)
model.compile(optimizer=optimizer, loss=model.compute_loss) # can also use any keras loss fn
model.fit(train_dataset.shuffle(1000).batch(16), epochs=3, batch_size=16)
```

Everything seems to go fine during fine-tuning, but when I try to predict on the test dataset (2000 samples) using `model.predict(test_dataset)`, I get an output with weird shape.

That is, instead of getting an output of shape (1, 2000, 100), I get one with shape (1, 1024000, 100), where 1024000 happens to be number of test examples (2000) times the sequence length (512).

Any hint on what's going on here? Sorry if it's a naïve mistake on my side, I'm new to tf.
One probably related issue is that if I try to use `model.evaluate` I get an error with the following traceback:
```

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-125-3f03cbe29a62> in <module>
----> 1 model.evaluate(test_dataset)

~/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py in _method_wrapper(self, *args, **kwargs)
    106   def _method_wrapper(self, *args, **kwargs):
    107     if not self._in_multi_worker_mode():  # pylint: disable=protected-access
--> 108       return method(self, *args, **kwargs)
    109 
    110     # Running inside `run_distribute_coordinator` already.

~/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py in evaluate(self, x, y, batch_size, verbose, sample_weight, steps, callbacks, max_queue_size, workers, use_multiprocessing, return_dict)
   1377             with trace.Trace('TraceContext', graph_type='test', step_num=step):
   1378               callbacks.on_test_batch_begin(step)
-> 1379               tmp_logs = test_function(iterator)
   1380               if data_handler.should_sync:
   1381                 context.async_wait()

~/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
    778       else:
    779         compiler = "nonXla"
--> 780         result = self._call(*args, **kwds)
    781 
    782       new_tracing_count = self._get_tracing_count()

~/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
    821       # This is the first call of __call__, so we have to initialize.
    822       initializers = []
--> 823       self._initialize(args, kwds, add_initializers_to=initializers)
    824     finally:
    825       # At this point we know that the initialization is complete (or less

~/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
    695     self._concrete_stateful_fn = (
    696         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access
--> 697             *args, **kwds))
    698 
    699     def invalid_creator_scope(*unused_args, **unused_kwds):

~/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
   2853       args, kwargs = None, None
   2854     with self._lock:
-> 2855       graph_function, _, _ = self._maybe_define_function(args, kwargs)
   2856     return graph_function
   2857 

~/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
   3211 
   3212       self._function_cache.missed.add(call_context_key)
-> 3213       graph_function = self._create_graph_function(args, kwargs)
   3214       self._function_cache.primary[cache_key] = graph_function
   3215       return graph_function, args, kwargs

~/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
   3073             arg_names=arg_names,
   3074             override_flat_arg_shapes=override_flat_arg_shapes,
-> 3075             capture_by_value=self._capture_by_value),
   3076         self._function_attributes,
   3077         function_spec=self.function_spec,

~/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)
    984         _, original_func = tf_decorator.unwrap(python_func)
    985 
--> 986       func_outputs = python_func(*func_args, **func_kwargs)
    987 
    988       # invariant: `func_outputs` contains only Tensors, CompositeTensors,

~/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)
    598         # __wrapped__ allows AutoGraph to swap in a converted function. We give
    599         # the function a weak reference to itself to avoid a reference cycle.
--> 600         return weak_wrapped_fn().__wrapped__(*args, **kwds)
    601     weak_wrapped_fn = weakref.ref(wrapped_fn)
    602 

~/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
    971           except Exception as e:  # pylint:disable=broad-except
    972             if hasattr(e, "ag_error_metadata"):
--> 973               raise e.ag_error_metadata.to_exception(e)
    974             else:
    975               raise

ValueError: in user code:

    /Users/rr48396/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:1224 test_function  *
        return step_function(self, iterator)
    /Users/rr48396/opt/anaconda3/lib/python3.7/site-packages/transformers/modeling_tf_utils.py:142 compute_loss  *
        return loss_fn(labels, logits)
    /Users/rr48396/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/keras/losses.py:149 __call__  **
        losses = ag_call(y_true, y_pred)
    /Users/rr48396/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/keras/losses.py:253 call  **
        return ag_fn(y_true, y_pred, **self._fn_kwargs)
    /Users/rr48396/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/util/dispatch.py:201 wrapper
        return target(*args, **kwargs)
    /Users/rr48396/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/keras/losses.py:1567 sparse_categorical_crossentropy
        y_true, y_pred, from_logits=from_logits, axis=axis)
    /Users/rr48396/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/util/dispatch.py:201 wrapper
        return target(*args, **kwargs)
    /Users/rr48396/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/keras/backend.py:4783 sparse_categorical_crossentropy
        labels=target, logits=output)
    /Users/rr48396/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/util/dispatch.py:201 wrapper
        return target(*args, **kwargs)
    /Users/rr48396/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/ops/nn_ops.py:4175 sparse_softmax_cross_entropy_with_logits_v2
        labels=labels, logits=logits, name=name)
    /Users/rr48396/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/util/dispatch.py:201 wrapper
        return target(*args, **kwargs)
    /Users/rr48396/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/ops/nn_ops.py:4090 sparse_softmax_cross_entropy_with_logits
        logits.get_shape()))

    ValueError: Shape mismatch: The shape of labels (received (1,)) should equal the shape of logits except for the last dimension (received (512, 100)).
```

Shouldn't be an error related to my dataset, as it's constructed the same way as in the tutorial...<|||||>never mind, I didn't realize the model requires manually batching the dataset at prediction. closing :) 