code: def __init__(self, c_in, d_model):
      super(FixedEmbedding, self).__init__()
  
      w = torch.zeros(c_in, d_model).float()
      w.require_grad = False
  
      position = torch.arange(0, c_in).float().unsqueeze(1)
      div_term = (
          torch.arange(0, d_model, 2).float()
          * -(math.log(10000.0) / d_model)
      ).exp()
  
      w[:, 0::2] = torch.sin(position * div_term)
      w[:, 1::2] = torch.cos(position * div_term)
  
      self.emb = nn.Embedding(c_in, d_model)
      self.emb.weight = nn.Parameter(w, requires_grad=False)
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn.utils import weight_norm
  import math
label_desc: Use image node_id 1 to create a new FixedEmbedding object from inherited base classes: nn with example: obj = FixedEmbedding(c_in, d_model)