```py
from transformers import TapasConfig, TapasForQuestionAnswering
you can initialize the classification heads any way you want (see docs of TapasConfig)
config = TapasConfig(num_aggregation_labels=3, average_logits_per_cell=True)
initializing the pre-trained base sized model with our custom classification heads
model = TapasForQuestionAnswering.from_pretrained("google/tapas-base", config=config)
===PT-TF-SPLIT===
from transformers import TapasConfig, TFTapasForQuestionAnswering
you can initialize the classification heads any way you want (see docs of TapasConfig)
config = TapasConfig(num_aggregation_labels=3, average_logits_per_cell=True)
initializing the pre-trained base sized model with our custom classification heads
model = TFTapasForQuestionAnswering.from_pretrained("google/tapas-base", config=config)
