Reduce memory usage in TF building
This PR reduces the default shape of dummy inputs from (3, 3) to (2, 2). This slightly reduces the memory usage when building TF models, which should hopefully fix some of our pipeline tests.

We could replace the dummy inputs with symbolic tensors, which would mean we could build TF models with 0 memory usage, but this would make TF model building slower (~4X) because it would implicitly compile the model when building, which is probably not an acceptable tradeoff.

cc @ydshieh and @amyeroberts as core maintainer
_The documentation is not available anymore as the PR was closed or merged._<|||||>Let me run it on CI and see.<|||||>Sorry for the delay - there's an issue with Funnel that wasn't reproducing on my machine. I eventually figured out that the problem is the classic TF one: indices for `tf.gather` are not validated on GPU but are validated on CPU, and so the bug only becomes apparent on CPU. Will fix in just a sec!<|||||>I also tried to run the change in this PR, and got 


```
FAILED tests/pipelines/test_pipelines_common.py::PipelineUtilsTest::test_load_default_pipelines_tf - tensorflow.python.framework.errors_impl.ResourceExhaustedError: {{function_node __wrapped__Transpose_device_/job:localhost/replica:0/task:0/device:GPU:0}} OOM when allocating tensor with shape[768,768] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc [Op:Transpose]
FAILED tests/pipelines/test_pipelines_common.py::PipelineUtilsTest::test_load_default_pipelines_tf_table_qa - tensorflow.python.framework.errors_impl.ResourceExhaustedError: Exception encountered when calling layer 'tapas' (type TFTapasMainLayer).

{{function_node __wrapped__StatelessTruncatedNormalV2_device_/job:localhost/replica:0/task:0/device:GPU:0}} OOM when allocating tensor with shape[30522,768] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc [Op:StatelessTruncatedNormalV2]

Call arguments received by layer 'tapas' (type TFTapasMainLayer):
  • input_ids=tf.Tensor(shape=(2, 2), dtype=int32)
  • attention_mask=tf.Tensor(shape=(2, 2), dtype=float32)
  • token_type_ids=tf.Tensor(shape=(2, 2, 7), dtype=int32)
  • position_ids=None
  • head_mask=None
  • inputs_embeds=None
  • output_attentions=False
  • output_hidden_states=False
  • return_dict=True
  • training=False
```
and 5 other ones (probably due to the above one).

@Rocketknight1 I think we will have to reiterate (change->run->change->run) a bit more before we merge.<|||||>Yep, working on it now!<|||||>The `tests/pipelines/test_pipelines_common.py::PipelineUtilsTest::test_load_default_pipelines_tf` run against a list of models, so it's kind normal it fails with other models even some fixes are done previously.

I am OK to trigger the run (a subset) whenever you feel it's time. Otherwise I can show you a modified workflow file for you to trigger manually.<|||||>@ydshieh the issues with Funnel have been resolved, so this should be ready for a CI run now!<|||||>You can watch it live [here](https://github.com/huggingface/transformers/actions/runs/5191137996/jobs/9358557442). It will take 20-30 min to finish.<|||||>Looks like they're still failing even with very small dummies. I'll investigate those models and try to figure out why - the new dummies should be smaller than the old ones! <|||||>Maybe this is a sign that we should transition the dummies to symbolic tensors for those models, even if it's probably too slow for our tests to do it across the whole codebase.