ActivationsExtractorHelper._package_layer infers wrong shape for 1x1 conv implemented as linear layers

ConvNeXt (and possibly other modern models) implement 1x1 convolutions as linear layers broadcasted over height and width because this is slightly more performant (according to a comment in the model repo).
E.g. from the [ConvNeXt implementation in the FacebookResearch Repo](https://github.com/facebookresearch/ConvNeXt/blob/main/models/convnext.py):
Line 30: `self.pwconv1 = nn.Linear(dim, 4 * dim) # pointwise/1x1 convs, implemented with linear layers`
In order to achieve this, dimensions are temporarily permuted:
Line 40: `x = x.permute(0, 2, 3, 1) # (N, C, H, W) -> (N, H, W, C)`

This leads to activations that match with the Conv2D case in the `_package_layer` function [line 281-282](https://github.com/brain-score/vision/blob/aa6797eac70b5dda70ef35653bdc9b5f5a56a089/brainscore_vision/model_helpers/activations/core.py#L281) but do not have channels first:
```
elif flatten_indices.shape[1] == 3: # 2DConv, e.g. resnet
flatten_coord_names = ['channel', 'channel_x', 'channel_y']
```
Thus for models that have committed a 1x1 conv layer for any brain region, the wrong shape is inferred.
E.g. for ConvNeXt_xlarge ([from the timm implementation, but same principle](https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/convnext.py)) V1-region, it infers
channels: 14
x-dim: 14
y-dim: 4096