```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

tokenizer = AutoTokenizer.from_pretrained('allenai/longformer-scico')
model = AutoModelForSequenceClassification.from_pretrained('allenai/longformer-scico')

start_token = tokenizer.convert_tokens_to_ids("<m>")
end_token = tokenizer.convert_tokens_to_aids("</m>")

def get_global_attention(input_ids):
    global_attention_mask = torch.zeros(input_ids.shape)
    global_attention_mask[:, 0] = 1  # global attention to the CLS token
    start = torch.nonzero(input_ids == start_token) # global attention to the <m> token
    end = torch.nonzero(input_ids == end_token) # global attention to the </m> token
    globs = torch.cat((start, end))
    value = torch.ones(globs.shape[0])
    global_attention_mask.index_put_(tuple(globs.t()), value)
    return global_attention_mask
    
   
```