class_docstr: Multi-Head Attention layer of Transformer-XL with sliding window local+global attention from Longformer.
  Partially adapted from allenai (https://github.com/allenai/longformer/blob/master/longformer/sliding_chunks.py)
  and huggingface (https://github.com/huggingface/transformers/blob/main/src/transformers/models/longformer/modeling_longformer.py) 
  Paper: https://arxiv.org/abs/1901.02860 (Transformer-XL),
         https://arxiv.org/abs/2004.05150 (Longformer)
  Args:
      n_head (int): number of heads
      n_feat (int): size of the features
      dropout_rate (float): dropout rate
      pos_bias_u (Tensor): the positional bias matrix U
      pos_bias_v (Tensor): the positional bias matrix V
      att_context_size (List[int]): List of 2 ints corresponding to left and right attention context sizes.
      max_cache_len (int): the maximum size of cache
      global_tokens (int): number of tokens to be used for global attention
      global_tokens_spacing (int): how far apart the global tokens are
      global_attn_separate (bool): whether the q, k, v layers used for global tokens should be separate
class_docstr_tok: ["Multi-Head","Attention","layer","of","Transformer-XL","with","sliding","window","local+global","attention","from","Longformer",".","Partially","adapted","from","allenai","(","https",":","\/\/github.com\/allenai\/longformer\/blob\/master\/longformer\/sliding_chunks.py",")","and","huggingface","(","https",":","\/\/github.com\/huggingface\/transformers\/blob\/main\/src\/transformers\/models\/longformer\/modeling_longformer.py",")","Paper",":","https",":","\/\/arxiv.org\/abs\/1901.02860","(","Transformer-XL",")",",","https",":","\/\/arxiv.org\/abs\/2004.05150","(","Longformer",")","Args",":","n_head","(","int",")",":","number","of","heads","n_feat","(","int",")",":","size","of","the","features","dropout_rate","(","float",")",":","dropout","rate","pos_bias_u","(","Tensor",")",":","the","positional","bias","matrix","U","pos_bias_v","(","Tensor",")",":","the","positional","bias","matrix","V","att_context_size","(","List","[","int","]",")",":","List","of","2","ints","corresponding","to","left","and","right","attention","context","sizes",".","max_cache_len","(","int",")",":","the","maximum","size","of","cache","global_tokens","(","int",")",":","number","of","tokens","to","be","used","for","global","attention","global_tokens_spacing","(","int",")",":","how","far","apart","the","global","tokens","are","global_attn_separate","(","bool",")",":","whether","the","q",",","k",",","v","layers","used","for","global","tokens","should","be","separate"]
code: def _get_invalid_locations_mask(self, w: int, device: str):
      diagonals_list = []
      for j in range(-w, 1):
          diagonal_mask = torch.zeros(
              w, device="cpu", dtype=torch.uint8
          )
          diagonal_mask[:-j] = 1
          diagonals_list.append(diagonal_mask)
  
      mask = torch.stack(diagonals_list, dim=-1)
      mask = mask[None, None, :, :]
  
      ending_mask = mask.flip(dims=(2, 3)).bool().to(device)
      return mask.bool().to(device), ending_mask
  
imports: import math
  from functools import lru_cache
  from typing import List, Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  from nemo.utils import avoid_float16_autocast_context
label_desc: Use image node_id 10 for calling the RelPositionMultiHeadAttentionLongformer obj's underlying member method code with example usage: obj._get_invalid_locations_mask(w, device) and returns: mask,  ending_mask