class_docstr: Pipeline for text-to-image generation using Kandinsky
  
  This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
  library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
  
  Args:
      scheduler (Union[`DDIMScheduler`,`DDPMScheduler`]):
          A scheduler to be used in combination with `unet` to generate image latents.
      unet ([`UNet2DConditionModel`]):
          Conditional U-Net architecture to denoise the image embedding.
      movq ([`VQModel`]):
          MoVQ Decoder to generate the image from the latents.
code: def num_timesteps(self):
      return self._num_timesteps
  
file_path: diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2.py
imports: from typing import Callable, Dict, List, Optional, Union
  import torch
  from ...models import UNet2DConditionModel, VQModel
  from ...schedulers import DDPMScheduler
  from ...utils import deprecate, logging, replace_example_docstring
  from ...utils.torch_utils import randn_tensor
  from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
label_desc: Use image node_id 5 for calling the KandinskyV22Pipeline obj's underlying member method code with example usage: obj.num_timesteps() and returns: self