code: def lerp(
      v0: Union[torch.Tensor, np.ndarray],
      v1: Union[torch.Tensor, np.ndarray],
      t: Union[float, torch.Tensor, np.ndarray],
  ) -> Union[torch.Tensor, np.ndarray]:
      """
      Linearly interpolate between two vectors/tensors.
  
      Args:
          v0 (`torch.Tensor` or `np.ndarray`): First vector/tensor.
          v1 (`torch.Tensor` or `np.ndarray`): Second vector/tensor.
          t: (`float`, `torch.Tensor`, or `np.ndarray`):
              Interpolation factor. If float, must be between 0 and 1. If np.ndarray or
              torch.Tensor, must be one dimensional with values between 0 and 1.
  
      Returns:
          Union[torch.Tensor, np.ndarray]
              Interpolated vector/tensor between v0 and v1.
      """
      inputs_are_torch = False
      t_is_float = False
  
      if isinstance(v0, torch.Tensor):
          inputs_are_torch = True
          input_device = v0.device
          v0 = v0.cpu().numpy()
          v1 = v1.cpu().numpy()
  
      if isinstance(t, torch.Tensor):
          inputs_are_torch = True
          input_device = t.device
          t = t.cpu().numpy()
      elif isinstance(t, float):
          t_is_float = True
          t = np.array([t])
  
      t = t[..., None]
      v0 = v0[None, ...]
      v1 = v1[None, ...]
      v2 = (1 - t) * v0 + t * v1
  
      if t_is_float and v0.ndim > 1:
          assert v2.shape[0] == 1
          v2 = np.squeeze(v2, axis=0)
      if inputs_are_torch:
          v2 = torch.from_numpy(v2).to(input_device)
  
      return v2
  
imports: import inspect
  from typing import Any, Callable, Dict, List, Optional, Union
  import numpy
  import torch
  from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
  from diffusers.image_processor import VaeImageProcessor
  from diffusers.loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin
  from diffusers.models import AutoencoderKL, UNet2DConditionModel
  from diffusers.models.lora import adjust_lora_scale_text_encoder
  from diffusers.pipelines.pipeline_utils import DiffusionPipeline
  from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput, StableDiffusionSafetyChecker
  from diffusers.schedulers import LCMScheduler
  from diffusers.utils import USE_PEFT_BACKEND, deprecate, logging, replace_example_docstring, scale_lora_layers, unscale_lora_layers
  from diffusers.utils.torch_utils import randn_tensor
label_desc: Use image node_id 1 for calling a global function with example usage: lerp(v0, v1, t) and returns: v2