class_docstr: Positional encoding with noise level conditioning
class_docstr_tok: ["Positional","encoding","with","noise","level","conditioning"]
code: def forward(self, x, noise_level):
      if x.shape[2] > self.pe.shape[1]:
          self.init_pe_matrix(x.shape[1], x.shape[2], x)
      return (
          x
          + noise_level[..., None, None]
          + self.pe[:, : x.size(2)].repeat(x.shape[0], 1, 1) / self.C
      )
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn.utils import weight_norm
label_desc: Use image node_id 2 for calling the PositionalEncoding obj's underlying member method code with example usage: obj.forward(x, noise_level) and returns: unknown