class_docstr: Pipeline for audio diffusion.
  
  This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
  implemented for all pipelines (downloading, saving, running on a particular device, etc.).
  
  Parameters:
      vqae ([`AutoencoderKL`]):
          Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
      unet ([`UNet2DConditionModel`]):
          A `UNet2DConditionModel` to denoise the encoded image latents.
      mel ([`Mel`]):
          Transform audio into a spectrogram.
      scheduler ([`DDIMScheduler`] or [`DDPMScheduler`]):
          A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
          [`DDIMScheduler`] or [`DDPMScheduler`].
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docstr: Spherical Linear intERPolation.
  
  Args:
      x0 (`torch.Tensor`):
          The first tensor to interpolate between.
      x1 (`torch.Tensor`):
          Second tensor to interpolate between.
      alpha (`float`):
          Interpolation between 0 and 1
  
  Returns:
      `torch.Tensor`:
          The interpolated tensor.
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code: def slerp(
      x0: torch.Tensor, x1: torch.Tensor, alpha: float
  ) -> torch.Tensor:
      """Spherical Linear intERPolation.
  
      Args:
          x0 (`torch.Tensor`):
              The first tensor to interpolate between.
          x1 (`torch.Tensor`):
              Second tensor to interpolate between.
          alpha (`float`):
              Interpolation between 0 and 1
  
      Returns:
          `torch.Tensor`:
              The interpolated tensor.
      """
  
      theta = acos(
          torch.dot(torch.flatten(x0), torch.flatten(x1))
          / torch.norm(x0)
          / torch.norm(x1)
      )
      return sin((1 - alpha) * theta) * x0 / sin(theta) + sin(
          alpha * theta
      ) * x1 / sin(theta)
  
file_path: llmga/llmga/diffusers/src/diffusers/pipelines/audio_diffusion/pipeline_audio_diffusion.py
imports: from math import acos, sin
  from typing import List, Tuple, Union
  import numpy
  import torch
  from PIL import Image
  from ...models import AutoencoderKL, UNet2DConditionModel
  from ...schedulers import DDIMScheduler, DDPMScheduler
  from ...utils.torch_utils import randn_tensor
  from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
  from .mel import Mel
label_desc: Use image node_id 5 for calling the AudioDiffusionPipeline obj's underlying member method code with example usage: obj.slerp(x0, x1, alpha) and returns: unknown