class_docstr: Models for Wind Power Prediction
      
code: def __init__(self, config):
      super(WPFModel, self).__init__()
      self.config_file = config
      self.var_len = config.var_len
      self.input_len = config.input_len
      self.output_len = config.output_len
      self.hidden_dims = config.model.hidden_dims
      self.capacity = config.capacity
  
      self.decomp = SeriesDecomp(DECOMP)
  
      self.t_emb = nn.Embedding(300, self.hidden_dims)
      self.w_emb = nn.Embedding(300, self.hidden_dims)
  
      self.t_dec_emb = nn.Embedding(300, self.hidden_dims)
      self.w_dec_emb = nn.Embedding(300, self.hidden_dims)
  
      self.pos_dec_emb = paddle.create_parameter(
          shape=[1, self.input_len + self.output_len, self.hidden_dims],
          dtype="float32",
      )
  
      self.pos_emb = paddle.create_parameter(
          shape=[1, self.input_len, self.hidden_dims], dtype="float32"
      )
  
      self.st_conv_encoder = SpatialTemporalConv(
          self.capacity, self.var_len, self.hidden_dims
      )
      self.st_conv_decoder = SpatialTemporalConv(
          self.capacity, self.var_len, self.hidden_dims
      )
  
      self.enc = Encoder(config)
      self.dec = Decoder(config)
  
      self.pred_nn = nn.Linear(self.hidden_dims, self.capacity)
      self.apply(self.init_weights)
  
imports: import pgl
  import paddle
  import paddle.nn
  import paddle.nn.functional
  import numpy
  from pgl.utils.logger import log
  import pgl.nn
  import math
label_desc: Use image node_id 1 to create a new WPFModel object from inherited base classes: nn with example: obj = WPFModel(config)