NSL for multiclass classification

I am working on multiclass classification using neural structured learning. I am trying to edit the code (for binary classification) for Graph regularization for sentiment classification using synthesized graphs  |  Neural Structured Learning  |  TensorFlow for my purpose. I am getting the following error. Can anyone please help?

File “/home/vinay/anaconda3/envs/newenvt/lib/python3.9/site-packages/tensorflow/python/eager/execute.py”, line 59, in quick_execute
tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
tensorflow.python.framework.errors_impl.InvalidArgumentError: Feature: NL_nbr_0_words (data type: float) is required but could not be found.
[[{{node ParseSingleExample/ParseExample/ParseExampleV2}}]]
[[IteratorGetNext]] [Op:__inference_train_function_1996]

I am getting datatype errors when I use one hot encoding for labels . So I switched to sparse categorical crossentropy. Am I doing something wrong? What else can be done for multiclass classification?