code: def __init__(self, dim, nhead, dropout=0.1, aux=False):
      super().__init__()
      self._smoothing_weight = nn.Parameter(torch.randn(nhead, 1))
      self.v0 = nn.Parameter(torch.randn(1, 1, nhead, dim))
      self.dropout = nn.Dropout(dropout)
      if aux:
          self.aux_dropout = nn.Dropout(dropout)
  
imports: import os
  import math
  import numpy
  from math import sqrt
  from scipy.fftpack import next_fast_len
label_desc: Use image node_id 1 to create a new ExponentialSmoothing object from inherited base classes: nn with example: obj = ExponentialSmoothing(dim, nhead, dropout, aux)