code: def __init__(
      self,
      d_model,
      nhead,
      d_head=None,
      dropout=0.1,
      output_attention=False,
  ):
      super().__init__()
      self.d_head = d_head or (d_model // nhead)
      self.d_model = d_model
      self.nhead = nhead
      self.output_attention = output_attention
  
      self.z0 = nn.Parameter(torch.randn(self.nhead, self.d_head))
      self.in_proj = nn.Linear(self.d_model, self.d_head * self.nhead)
      self.es = ExponentialSmoothing(
          self.d_head, self.nhead, dropout=dropout
      )
      self.out_proj = nn.Linear(self.d_head * self.nhead, self.d_model)
  
      assert (
          self.d_head * self.nhead == self.d_model
      ), "d_model must be divisible by nhead"
  
imports: import numpy
  import math
  from math import sqrt
  import os
  from scipy.fftpack import next_fast_len
  from merlion.models.utils.nn_modules.blocks import ExponentialSmoothing, conv1d_fft
label_desc: Use image node_id 1 to create a new GrowthLayer object from inherited base classes: nn with example: obj = GrowthLayer(d_model, nhead, d_head, dropout, output_attention)