Longformer get last_hidden_state
I am trying to follow this example in the huggingface documentation here https://huggingface.co/transformers/model_doc/longformer.html:
import torch
from transformers import LongformerModel, LongformerTokenizer
model = LongformerModel.from_pretrained('allenai/longformer-base-4096')
tokenizer = LongformerTokenizer.from_pretrained('allenai/longformer-base-4096')
SAMPLE_TEXT = ' '.join(['Hello world! '] * 1000)  # long input document
input_ids = torch.tensor(tokenizer.encode(SAMPLE_TEXT)).unsqueeze(0)  # batch of size 1
# Attention mask values -- 0: no attention, 1: local attention, 2: global attention
attention_mask = torch.ones(input_ids.shape, dtype=torch.long, device=input_ids.device) # initialize to local attention
global_attention_mask = torch.zeros(input_ids.shape, dtype=torch.long, device=input_ids.device) # initialize to global attention to be deactivated for all tokens
global_attention_mask[:, [1, 4, 21,]] = 1  # Set global attention to random tokens for the sake of this example
                                    # Usually, set global attention based on the task. For example,
                                    # classification: the &lt;s&gt; token
                                    # QA: question tokens
                                    # LM: potentially on the beginning of sentences and paragraphs
outputs = model(input_ids, attention_mask=attention_mask, global_attention_mask=global_attention_mask, output_hidden_states= True)
sequence_output = outputs[0].last_hidden_state
pooled_output = outputs.pooler_output

I suppose that this would return a document embedding for the sample text.
However, I run into the following error:
AttributeError: 'Tensor' object has no attribute 'last_hidden_state'

Why isnt it possible to call last_hidden_state?

Do not select via index:
sequence_output = outputs.last_hidden_state

outputs is a LongformerBaseModelOutputWithPooling object with the following properties:
print(outputs.keys())

Output:
odict_keys(['last_hidden_state', 'pooler_output', 'hidden_states'])

Calling outputs[0] or outputs.last_hidden_state will both give you the same tensor, but this tensor does not have a property called last_hidden_state.
