tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
model = GPT2LMHeadModel.from_pretrained("gpt2")
inputs = tokenizer("Hello, my dog is cute and ", return_tensors="pt")
generation_output = model.generate(**inputs, return_dict_in_generate=True, output_scores=True)
```
The generation_output object is a [~generation_utils.GreedySearchDecoderOnlyOutput], as we can
see in the documentation of that class below, it means it has the following attributes:
sequences: the generated sequences of tokens
scores (optional): the prediction scores of the language modelling head, for each generation step
hidden_states (optional): the hidden states of the model, for each generation step
attentions (optional): the attention weights of the model, for each generation step
Here we have the scores since we passed along output_scores=True, but we don't have hidden_states and
