Dimensions Mismatch

Hello everyone,

I have been trying to run some inferences with the GenCast Mini Model, but during the predictions part I keep getting an error on dimensions mismatch between the checkpoint provided for the mini model and the way my script is processing the variables. Here is the exact error: ValueError: 'grid2mesh_gnn/~_networks_builder/encoder_nodes_grid_nodes_mlp/~/linear_0/w' with retrieved shape (267, 512) does not match shape=[333, 512] dtype=dtype('float32').

This is similar to the issue raised in here: [](https://github.com/google-deepmind/graphcast/issues/131)

Looking forward to some engaging discussions.

Thanks.

Start by reproducing the GenCast Mini Model inference failure during the prediction step and compare the checkpoint parameter `grid2mesh_gnn/~_networks_builder/encoder_nodes_grid_nodes_mlp/~/linear_0/w` with the reported shapes. Review the related GraphCast issue 131; done means identifying and resolving the checkpoint-versus-input dimension mismatch.

Written by the indexing model from the issue text.