class_docstr: Embeds the diffusion step into an input vector of DiffWave
  
  Arguments
  ---------
  max_steps: int
      total difussion steps
  
  Example
  -------
  >>> from speechbrain.lobes.models.DiffWave import DiffusionEmbedding
  >>> diffusion_embedding = DiffusionEmbedding(max_steps=50)
  >>> time_step = torch.randint(50, (1,))
  >>> step_embedding = diffusion_embedding(time_step)
  >>> step_embedding.shape
  torch.Size([1, 512])
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code: def __init__(self, max_steps):
      super().__init__()
      self.register_buffer(
          "embedding",
          self._build_embedding(max_steps),
          persistent=False,
      )
      self.projection1 = Linear(input_size=128, n_neurons=512)
      self.projection2 = Linear(input_size=512, n_neurons=512)
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from speechbrain.nnet.CNN import Conv1d
  from speechbrain.nnet import linear
  from speechbrain.nnet.diffusion import DenoisingDiffusion
  from math import sqrt
  from torchaudio import transforms
label_desc: Use image node_id 1 to create a new DiffusionEmbedding object from inherited base classes: nn with example: obj = DiffusionEmbedding(max_steps)