Thanks, this worked.
For info, as far as split batching goes I utilised sklearn.model_selection.train_test_split(*arrays, **options)  and torch.utils.data option to accept a variable number of arguments, and implementation looks like
# train/test data split 
seq_train, seq_test, static_train, static_test, train_y, test_y =  train_test_split(
    sequences, static_features, y, train_size=0.666, random_state=666)

# create tensor dataset
train_data = TensorDataset(
           torch.from_numpy(seq_train), torch.from_numpy(static_train), 
           torch.from_numpy(np.array(train_y))
)

# create dataloaders
train_loader = DataLoader(
             train_data, shuffle=True, 
             batch_size=666, drop_last=True)