class_docstr: Pipeline for image generation based on a Transformer backbone instead of a UNet.
  
  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:
      transformer ([`Transformer2DModel`]):
          A class conditioned `Transformer2DModel` to denoise the encoded image latents.
      vae ([`AutoencoderKL`]):
          Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
      scheduler ([`DDIMScheduler`]):
          A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
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docstr: Map label strings from ImageNet to corresponding class ids.
  
  Parameters:
      label (`str` or `dict` of `str`):
          Label strings to be mapped to class ids.
  
  Returns:
      `list` of `int`:
          Class ids to be processed by pipeline.
docstr_tok: ["Map","label","strings","from","ImageNet","to","corresponding","class","ids",".","Parameters",":","label","(","`","str","`","or","`","dict","`","of","`","str","`",")",":","Label","strings","to","be","mapped","to","class","ids",".","Returns",":","`","list","`","of","`","int","`",":","Class","ids","to","be","processed","by","pipeline","."]
code: def get_label_ids(self, label: Union[str, List[str]]) -> List[int]:
      r"""
  
      Map label strings from ImageNet to corresponding class ids.
  
      Parameters:
          label (`str` or `dict` of `str`):
              Label strings to be mapped to class ids.
  
      Returns:
          `list` of `int`:
              Class ids to be processed by pipeline.
      """
  
      if not isinstance(label, list):
          label = list(label)
  
      for l in label:
          if l not in self.labels:
              raise ValueError(
                  f"{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."
              )
  
      return [self.labels[l] for l in label]
  
file_path: diffusers/src/diffusers/pipelines/dit/pipeline_dit.py
imports: from typing import Dict, List, Optional, Tuple, Union
  import torch
  from ...models import AutoencoderKL, Transformer2DModel
  from ...schedulers import KarrasDiffusionSchedulers
  from ...utils.torch_utils import randn_tensor
  from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
label_desc: Use image node_id 2 for calling the DiTPipeline obj's underlying member method code with example usage: obj.get_label_ids(label) and returns: unknown