code: def test_timestep_embeddings(self):
      embedding_dim = 256
      timesteps = torch.arange(16)
  
      t1 = get_timestep_embedding(timesteps, embedding_dim)
  
      # first vector should always be composed only of 0's and 1's
      assert (t1[0, : embedding_dim // 2] - 0).abs().sum() < 1e-5
      assert (t1[0, embedding_dim // 2 :] - 1).abs().sum() < 1e-5
  
      # last element of each vector should be one
      assert (t1[:, -1] - 1).abs().sum() < 1e-5
  
      # For large embeddings (e.g. 128) the frequency of every vector is higher
      # than the previous one which means that the gradients of later vectors are
      # ALWAYS higher than the previous ones
      grad_mean = np.abs(np.gradient(t1, axis=-1)).mean(axis=1)
  
      prev_grad = 0.0
      for grad in grad_mean:
          assert grad > prev_grad
          prev_grad = grad
  
imports: import unittest
  import numpy
  import torch
  from torch import nn
  from diffusers.models.attention import GEGLU, AdaLayerNorm, ApproximateGELU
  from diffusers.models.embeddings import get_timestep_embedding
  from diffusers.models.lora import LoRACompatibleLinear
  from diffusers.models.resnet import Downsample2D, ResnetBlock2D, Upsample2D
  from diffusers.models.transformer_2d import Transformer2DModel
  from diffusers.utils.testing_utils import torch_device
label_desc: Use image node_id 1 for calling the EmbeddingsTests obj's underlying member method code with example usage: obj.test_timestep_embeddings() without return types