code: def __init__(self, in_features, out_features, std=1.0):
      super().__init__()
      assert out_features % 2 == 0
      # self.weight = nn.Parameter(paddle.randn([out_features // 2, in_features]) * std)
      self.weight = paddle.create_parameter(
          [out_features // 2, in_features],
          dtype="float32",
          default_initializer=nn.initializer.Normal(mean=0.0, std=std),
      )
  
imports: import math
  from dataclasses import dataclass
  from functools import partial
  import paddle
  import paddle.nn
label_desc: Use image node_id 1 to create a new FourierFeatures object from inherited base classes: nn with example: obj = FourierFeatures(in_features, out_features, std)