code: def __init__(
      self, correlation, hidden_size, n_head, d_keys=None, d_values=None
  ):
      super(AutoCorrelationLayer, self).__init__()
  
      d_keys = d_keys or (hidden_size // n_head)
      d_values = d_values or (hidden_size // n_head)
  
      self.inner_correlation = correlation
      self.query_projection = nn.Linear(hidden_size, d_keys * n_head)
      self.key_projection = nn.Linear(hidden_size, d_keys * n_head)
      self.value_projection = nn.Linear(hidden_size, d_values * n_head)
      self.out_projection = nn.Linear(d_values * n_head, hidden_size)
      self.n_head = n_head
  
imports: import numpy
  from typing import Optional
  import torch
  import torch.nn
  import torch.nn.functional
  from ..common._modules import DataEmbedding
  from ..common._base_windows import BaseWindows
  from ..losses.pytorch import MAE
label_desc: Use image node_id 1 to create a new AutoCorrelationLayer object from inherited base classes: nn with example: obj = AutoCorrelationLayer(correlation, hidden_size, n_head, d_keys, d_values)