class_docstr: Gaussian Fourier embeddings for noise levels.
class_docstr_tok: ["Gaussian","Fourier","embeddings","for","noise","levels","."]
code: def __init__(self, embedding_size=256, scale=1.0):
      super().__init__()
      self.W = nn.Parameter(
          torch.randn(embedding_size) * scale, requires_grad=False
      )
  
imports: import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from espnet2.enh.layers.ncsnpp_utils import layers, up_or_down_sampling
label_desc: Use image node_id 1 to create a new GaussianFourierProjection object from inherited base classes: nn with example: obj = GaussianFourierProjection(embedding_size, scale)