Hello, I am currently using this library: All I do so far is load data from a function they provide: ```py import torch import matplotlib.pyplot as plt import sys from neuralop.models import SFNO from neuralop import Trainer from neuralop.datasets import load_spherical_swe from neuralop.utils import count_model_params from neuralop import LpLoss, H1Loss device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') train_loader, test_loaders = load_spherical_swe(n_train=200, batch_size=4, train_resolution=(32, 64), test_resolutions=[(32, 64), (64, 128)], n_tests=[50, 50], test_batch_sizes=[10, 10],) dataset = train_loader.dataset ```` The dataset contains 200 samples. Each sample is a dict with a `x` and `y` key but apparently, I can use any arbritary key on the dataset: (I truncated the output of the data a lot) ```` (Pdb) type(dataset) (Pdb) dataset['randomkey42'] {'x': tensor([[[-0.3880, -0.3880, -0.3880, ..., -0.3880, -0.3880, -0.3880], [ 0.0442, -0.0937, -0.2294, ..., 0.4475, 0.3173, 0.1821], [-0.1630, -0.4303, -0.6900, ..., 0.6531, 0.3814, 0.1085], ..., [-0.5815, -0.4723, -0.3303, ..., -0.6264, -0.6633, -0.6476], [-0.1462, -0.1001, -0.0480, ..., -0.2411, -0.2167, -0.1852], [-0.3173, -0.3173, -0.3173, ..., -0.3173, -0.3173, -0.3173]]), 'y': tensor([[[-0.4424, -0.4424, -0.4424, ..., -0.4424, -0.4424, -0.4424], [-0.3909, -0.4150, -0.4414, ..., -0.3440, -0.3545, -0.3704], [-0.2927, -0.4489, -0.6083, ..., 0.0711, -0.0253, -0.1487],}, ``` and I don't get why.
They implemented their own getitem dunder (which grabs the var[stuff]) but... They don't use the index (the thing inside the []) at all in it Basically no matter what you put in the [], you get an unrelated sample