code: def __init__(self, B, L, device="cpu"):
      mask_shape = [B, 1, L, L]
      with torch.no_grad():
          self._mask = torch.triu(
              torch.ones(mask_shape, dtype=torch.bool), diagonal=1
          ).to(device)
  
file_path: neuralforecast/neuralforecast/models/vanillatransformer.py
imports: import math
  import numpy
  from typing import Optional
  import torch
  import torch.nn
  from neuralforecast.common._modules import TransEncoderLayer, TransEncoder, TransDecoderLayer, TransDecoder, DataEmbedding, AttentionLayer
  from ..common._base_windows import BaseWindows
  from ..losses.pytorch import MAE
label_desc: Use image node_id 1 to create a new TriangularCausalMask object  with example: obj = TriangularCausalMask(B, L, device)