class_docstr: Pipeline for unconditional audio generation.
  
  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.).
  
  Args:
      notes_encoder ([`SpectrogramNotesEncoder`]):
      continuous_encoder ([`SpectrogramContEncoder`]):
      decoder ([`T5FilmDecoder`]):
          A [`T5FilmDecoder`] to denoise the encoded audio latents.
      scheduler ([`DDPMScheduler`]):
          A scheduler to be used in combination with `decoder` to denoise the encoded audio latents.
      melgan ([`OnnxRuntimeModel`]):
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docstr: Invert by linearly scaling network outputs to features range.
docstr_tok: ["Invert","by","linearly","scaling","network","outputs","to","features","range","."]
code: def scale_to_features(
      self, outputs, input_range=(-1.0, 1.0), clip=False
  ):
      """Invert by linearly scaling network outputs to features range."""
      min_out, max_out = input_range
      outputs = (
          torch.clip(outputs, min_out, max_out) if clip else outputs
      )
      # Scale to [0, 1].
      zero_one = (outputs - min_out) / (max_out - min_out)
      # Scale to [self.min_value, self.max_value].
      return (
          zero_one * (self.max_value - self.min_value) + self.min_value
      )
  
file_path: llmga/llmga/diffusers/src/diffusers/pipelines/spectrogram_diffusion/pipeline_spectrogram_diffusion.py
imports: import math
  from typing import Any, Callable, List, Optional, Tuple, Union
  import numpy
  import torch
  from ...models import T5FilmDecoder
  from ...schedulers import DDPMScheduler
  from ...utils import is_onnx_available, logging
  from ...utils.torch_utils import randn_tensor
  from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
  from .continous_encoder import SpectrogramContEncoder
  from .notes_encoder import SpectrogramNotesEncoder
label_desc: Use image node_id 3 for calling the SpectrogramDiffusionPipeline obj's underlying member method code with example usage: obj.scale_to_features(outputs, input_range, clip) and returns: unknown