code: def forward(self, x, x_mark=None):
      # Convolution
      x = self.value_embedding(x)
  
      # Add positional (relative withing window) embedding with sines and cosines
      if self.position_embedding is not None:
          x = x + self.position_embedding(x)
  
      # Add temporal (absolute in time series) embedding with linear layer
      if self.temporal_embedding is not None:
          x = x + self.temporal_embedding(x_mark)
  
      return self.dropout(x)
  
imports: import math
  import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the DataEmbedding obj's underlying member method code with example usage: obj.forward(x, x_mark) and returns: self