code: def timestep_embedding(
      timesteps, dim, max_period=10000, repeat_only=False
  ):
      """
      Create sinusoidal timestep embeddings.
  
      Parameters:
          timesteps (Tensor): A 1-D tensor of N indices, one per batch element. These indices may be fractional and
                              represent the timesteps for which embeddings are to be created.
          dim (int): The dimension of the output embeddings. Each timestep will be represented as a vector of this dimension.
          max_period (float): Controls the minimum frequency of the embeddings. Higher values result in higher frequency
                              components in the embedding.
  
      Returns:
          Tensor: An [N x dim] tensor of positional embeddings, where each row corresponds to the embedding for a timestep.
      """
  
      if not repeat_only:
          half = dim // 2
          idx = get_idx(half, timesteps.device)
          freqs = torch.exp(-math.log(max_period) / half * idx)
          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,
              )
      else:
          embedding = repeat(timesteps, "b -> b d", d=dim)
      return embedding
  
imports: import math
  import numpy
  import torch
  import torch.nn
  from apex.contrib.group_norm import GroupNorm
  from einops import repeat
  from torch._dynamo import disable
  from torch.cuda.amp import custom_bwd, custom_fwd
label_desc: Use image node_id 8 for calling a global function with example usage: timestep_embedding(timesteps, dim, max_period, repeat_only) and returns: embedding