docstr: See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
  
  Args:
      timesteps (`torch.Tensor`):
          generate embedding vectors at these timesteps
      embedding_dim (`int`, *optional*, defaults to 512):
          dimension of the embeddings to generate
      dtype:
          data type of the generated embeddings
  
  Returns:
      `torch.FloatTensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)`
code: def get_guidance_scale_embedding(
      self, w, embedding_dim=512, dtype=torch.float32
  ):
      """
      See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
  
      Args:
          timesteps (`torch.Tensor`):
              generate embedding vectors at these timesteps
          embedding_dim (`int`, *optional*, defaults to 512):
              dimension of the embeddings to generate
          dtype:
              data type of the generated embeddings
  
      Returns:
          `torch.FloatTensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)`
      """
      assert len(w.shape) == 1
      w = w * 1000.0
  
      half_dim = embedding_dim // 2
      emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)
      emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)
      emb = w.to(dtype)[:, None] * emb[None, :]
      emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
      if embedding_dim % 2 == 1:  # zero pad
          emb = torch.nn.functional.pad(emb, (0, 1))
      assert emb.shape == (w.shape[0], embedding_dim)
      return emb
  
imports: import inspect
  from typing import Any, Dict, List, Optional, Union
  import torch
  import torch.nn
  from transformers import AutoModel, AutoTokenizer, CLIPImageProcessor
  from diffusers import DiffusionPipeline
  from diffusers.image_processor import VaeImageProcessor
  from diffusers.loaders import LoraLoaderMixin
  from diffusers.models import AutoencoderKL, UNet2DConditionModel
  from diffusers.models.lora import adjust_lora_scale_text_encoder
  from diffusers.pipelines.stable_diffusion.pipeline_output import StableDiffusionPipelineOutput
  from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
  from diffusers.schedulers import KarrasDiffusionSchedulers
  from diffusers.utils import USE_PEFT_BACKEND, logging, scale_lora_layers, unscale_lora_layers
  from diffusers.utils.torch_utils import randn_tensor
label_desc: Use image node_id 15 for calling the GlueGenStableDiffusionPipeline obj's underlying member method code with example usage: obj.get_guidance_scale_embedding(w, embedding_dim, dtype) and returns: emb