### `AutoModelForTableQuestionAnswering`


The `AutoModelForTableQuestionAnswering` class is a generic model that will be instantiated as one of the model classes of the Hugging Face Transformers library (with a table question answering head) when created with either the `from_pretrained()` or `from_config()` methods.

This class **cannot be instantiated directly** using the `__init__()` method. Attempting to do so will raise an error.

#### Methods

##### 1. `from_config`

**Definition**:
```python
AutoModelForTableQuestionAnswering.from_config(**kwargs)
```

**Parameters**:
- **config (PretrainedConfig)**: The model class to instantiate is selected based on the configuration class. For example:
  - `TapasConfig` will instantiate the `TapasForQuestionAnswering` model (TAPAS model).

Note: Loading a model from its configuration file does not load the model weights; it only modifies the model’s configuration. Use `from_pretrained()` to load the model weights.

**Example**:
```python
from transformers import AutoConfig, AutoModelForTableQuestionAnswering

# Download configuration from Hugging Face and cache it
config = AutoConfig.from_pretrained('google/tapas-base-finetuned-wtq')
model = AutoModelForTableQuestionAnswering.from_config(config)
```

##### 2. `from_pretrained`

**Definition**:
```python
AutoModelForTableQuestionAnswering.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
```

**Parameters**:
- **pretrained_model_name_or_path (str or os.PathLike)**: Can be one of the following:
  - A model id string (e.g., `bert-base-uncased`) or a path to a directory containing model weights saved with `save_pretrained()`.
  - A TensorFlow checkpoint file path (e.g., `./tf_model/model.ckpt.index`), in which case `from_tf` should be set to `True`.
  
- **model_args (optional)**: Additional positional arguments passed to the model's `__init__()` method.

- **config (PretrainedConfig, optional)**: Configuration to use instead of automatically loading the configuration.

- **state_dict (Dict[str, torch.Tensor], optional)**: The state dictionary to use instead of the one loaded from the pretrained weights file.

- **cache_dir (str or os.PathLike, optional)**: Path to the cache directory for storing downloaded pretrained configurations.

- **from_tf (bool, optional)**: If set to `True`, will load the model weights from a TensorFlow checkpoint file.

- **force_download (bool, optional)**: Whether to force (re-)download of model weights and configuration files.

- **resume_download (bool, optional)**: Whether to resume incomplete downloads.

- **proxies (Dict[str, str], optional)**: Proxy configuration for HTTP requests.

- **output_loading_info (bool, optional)**: Whether to return loading information, like missing keys or errors.

- **local_files_only (bool, optional)**: If set to `True`, only loads files from local storage.

- **revision (str, optional)**: The version of the model to use (e.g., branch name, tag name, or commit id).

- **trust_remote_code (bool, optional)**: Whether to allow executing custom model code from the Hub.

- **kwargs (optional)**: Additional keyword arguments passed to the model's `__init__()` method or configuration class.

**Example**:
```python
from transformers import AutoModelForTableQuestionAnswering

# Load model and configuration from Hugging Face
model = AutoModelForTableQuestionAnswering.from_pretrained('google/tapas-base-finetuned-wtq')

# Update configuration during loading
model = AutoModelForTableQuestionAnswering.from_pretrained('google/tapas-base-finetuned-wtq', output_attentions=True)
print(model.config.output_attentions)  # Output: True

# Load from a TensorFlow checkpoint (slower process)
config = AutoConfig.from_pretrained('./tf_model/tapas_tf_model_config.json')
model = AutoModelForTableQuestionAnswering.from_pretrained('./tf_model/tapas_tf_checkpoint.ckpt.index', from_tf=True, config=config)
```

### Summary
- `AutoModelForTableQuestionAnswering` is designed for creating model instances for table question answering tasks based on pretrained configurations or model weights.
- It automatically selects the appropriate model class based on the configuration or model id provided.
- The model can be loaded from the Hugging Face Model Hub or from local directories and TensorFlow checkpoints.