code: def timestep_embedding(timesteps, dim, max_period=10000):
      """
      Create sinusoidal timestep embeddings.
  
      Arguments
      ---------
      timesteps: torch.Tensor
          a 1-D Tensor of N indices, one per batch element. These may be fractional.
      dim: int
          the dimension of the output.
      max_period: int
          controls the minimum frequency of the embeddings.
  
      Returns
      -------
      result: torch.Tensor
           an [N x dim] Tensor of positional embeddings.
      """
      half = dim // 2
      freqs = torch.exp(
          -math.log(max_period)
          * torch.arange(start=0, end=half, dtype=torch.float32)
          / half
      ).to(device=timesteps.device)
      args = timesteps[:, None].float() * freqs[None]
      embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
      if dim % 2:
          embedding = torch.cat(
              [embedding, torch.zeros_like(embedding[:, :1])], dim=-1
          )
      return embedding
  
imports: from abc import abstractmethod
  from speechbrain.utils.data_utils import pad_divisible
  from .autoencoders import NormalizingAutoencoder
  import math
  import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 4 for calling a global function with example usage: timestep_embedding(timesteps, dim, max_period) and returns: embedding