code: def __init__(
      self,
      mask_flag=True,
      scale=None,
      attention_dropout=0.1,
      output_attention=False,
  ):
      super(FullAttention, self).__init__()
      self.scale = scale
      self.mask_flag = mask_flag
      self.output_attention = output_attention
      self.dropout = nn.Dropout(attention_dropout)
  
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 FullAttention object from inherited base classes: nn with example: obj = FullAttention(mask_flag, scale, attention_dropout, output_attention)