class_docstr: Gaussian Fourier embeddings for noise levels.
class_docstr_tok: ["Gaussian","Fourier","embeddings","for","noise","levels","."]
code: def forward(self, x):
      x_proj = x[:, None] * self.W[None, :] * 2 * np.pi
      return torch.cat([torch.sin(x_proj), torch.cos(x_proj)], dim=-1)
  
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 2 for calling the GaussianFourierProjection obj's underlying member method code with example usage: obj.forward(x) and returns: torch