code: def __init__(self, c_in, d_model):
      super(TokenEmbedding, self).__init__()
      padding = 1 if torch.__version__ >= "1.5.0" else 2
      self.tokenConv = nn.Conv1d(
          in_channels=c_in,
          out_channels=d_model,
          kernel_size=3,
          padding=padding,
          padding_mode="circular",
          bias=False,
      )
      for m in self.modules():
          if isinstance(m, nn.Conv1d):
              nn.init.kaiming_normal_(
                  m.weight, mode="fan_in", nonlinearity="leaky_relu"
              )
  
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 TokenEmbedding object from inherited base classes: nn with example: obj = TokenEmbedding(c_in, d_model)