Repository: understandable-machine-intelligence-lab/Quantus
Issue: Remove "feature importance" requirement (i.e., explanations share shape with input)
Root cause: Validation Gap

### Description of the problem
- Many Quantus metrics practically work with non "feature importance" explanations (i.e., can accept free form explaantions

### Description of a solution
1. Remove unnecessary asserts in this one that runs for each `base.Metric`, see the commented out below:

```python
def assert_attributions(x_batch: np.array, a_batch: np.array) -> None:
    """
    Asserts on attributions, assumes channel first layout.

    Parameters
    ----------
    x_batch: np.ndarray
         The batch of input to compare the shape of the attributions with.
    a_batch: np.ndarray
         The batch of attributions.

    Returns
    -------
    None
    """
    assert (
        type(a_batch) == np.ndarray
    ), "Attributions 'a_batch' should be of type np.ndarray."
    assert np.shape(x_batch)[0] == np.shape(a_batch)[0], (
        "The inputs 'x_batch' and attributions 'a_batch' should "
        "include the same number of samples."
        "{} != {}".format(np.shape(x_batch)[0], np.shape(a_batch)[0])
    )
    assert np.ndim(x_batch) == np.ndim(a_batch), (
        "The inputs 'x_batch' and attributions 'a_batch' should "
        "have the same number of dimensions."
        "{} != {}".format(np.ndim(x_batch), np.ndim(a_batch))
    )
    a_shape = [s for s in np.shape(a_batch)[1:] if s != 1]
    x_shape = [s for s in np.shape(x_batch)[1:]]
    #assert a_shape[0] == x_shape[0] or a_shape[-1] == x_shape[-1], (
     #   "The dimensions of attribution and input per sample should correspond in either "
      #  "the first or last dimensions, but got shapes "
     #   "{} and {}".format(a_shape, x_shape)
    #) # remove this!
    assert all([a in x_shape for a in a_shape]), (
        "All attribution dimensions should be included in the input dimensions, "
        "but got shapes {} and {}".format(a_shape, x_shape)
    )
    assert all(
        [
            x_shape.index(a) > x_shape.index(a_shape[i])
            for a in a_shape
            for i in range(a_shape.index(a))
        ]
    ), (
        "The dimensions of the attribution must correspond to dimensions of the input in the same order, "
        "but got shapes {} and {}".format(a_shape, x_shape)
    )
    #assert not np.all((a_batch == 0)), (
    #    "The elements in the attribution vector are all equal to zero, "
    #    "which may cause inconsistent results since many metrics rely on ordering. "
    #    "Recompute the explanations."
    #) # raise warning instead
    #assert not np.all((a_batch == 1.0)), (
    #    "The elements in the attribution vector are all equal to one, "
    #    "which may cause inconsistent results since many metrics rely on ordering. "
    #    "Recompute the explanations."
    #) # raise warning instead
    assert len(set(a_batch.flatten().tolist())) > 1, (
        "The attributions are uniformly distributed, "
        "which may cause inconsistent results since many "
        "metrics rely on ordering."
        "Recompute the explanations."
    )
    # assert not np.all((a_batch < 0.0)), "Attributions should not all be less than zero." # raise warning instead!
```

2. Add a new category to each metric, to clarity if the same shape is needed
```python
data_applicability = {DataType.IMAGE, DataType.TIMESERIES, DataType.TABULAR}
model_applicability = {ModelType.TORCH, ModelType.TF}
explanation_applicability = {ExplanationType.FeatureImportance, ExplanationType.FreeForm.....}
.....
```

### Minimum acceptance criteria
- Specify what is necessary for the issue to be closed.
- @mentions of the person that is apt to review these changes e.g., @annahedstroem