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:
      text_encoder ([`MultilingualCLIP`]):
          Frozen text-encoder.
      tokenizer ([`XLMRobertaTokenizer`]):
          Tokenizer of class
      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 _encode_prompt(
      self,
      prompt,
      device,
      num_images_per_prompt,
      do_classifier_free_guidance,
      negative_prompt=None,
  ):
      batch_size = len(prompt) if isinstance(prompt, list) else 1
      # get prompt text embeddings
      text_inputs = self.tokenizer(
          prompt,
          padding="max_length",
          truncation=True,
          max_length=77,
          return_attention_mask=True,
          add_special_tokens=True,
          return_tensors="pt",
      )
  
      text_input_ids = text_inputs.input_ids
      untruncated_ids = self.tokenizer(
          prompt, padding="longest", return_tensors="pt"
      ).input_ids
  
      if untruncated_ids.shape[-1] >= text_input_ids.shape[
          -1
      ] and not torch.equal(text_input_ids, untruncated_ids):
          removed_text = self.tokenizer.batch_decode(
              untruncated_ids[
                  :, self.tokenizer.model_max_length - 1 : -1
              ]
          )
          logger.warning(
              "The following part of your input was truncated because CLIP can only handle sequences up to"
              f" {self.tokenizer.model_max_length} tokens: {removed_text}"
          )
  
      text_input_ids = text_input_ids.to(device)
      text_mask = text_inputs.attention_mask.to(device)
  
      prompt_embeds, text_encoder_hidden_states = self.text_encoder(
          input_ids=text_input_ids, attention_mask=text_mask
      )
  
      prompt_embeds = prompt_embeds.repeat_interleave(
          num_images_per_prompt, dim=0
      )
      text_encoder_hidden_states = (
          text_encoder_hidden_states.repeat_interleave(
              num_images_per_prompt, dim=0
          )
      )
      text_mask = text_mask.repeat_interleave(
          num_images_per_prompt, dim=0
      )
  
      if do_classifier_free_guidance:
          uncond_tokens: List[str]
          if negative_prompt is None:
              uncond_tokens = [""] * batch_size
          elif type(prompt) is not type(negative_prompt):
              raise TypeError(
                  f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
                  f" {type(prompt)}."
              )
          elif isinstance(negative_prompt, str):
              uncond_tokens = [negative_prompt]
          elif batch_size != len(negative_prompt):
              raise ValueError(
                  f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
                  f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
                  " the batch size of `prompt`."
              )
          else:
              uncond_tokens = negative_prompt
  
          uncond_input = self.tokenizer(
              uncond_tokens,
              padding="max_length",
              max_length=77,
              truncation=True,
              return_attention_mask=True,
              add_special_tokens=True,
              return_tensors="pt",
          )
          uncond_text_input_ids = uncond_input.input_ids.to(device)
          uncond_text_mask = uncond_input.attention_mask.to(device)
  
          (
              negative_prompt_embeds,
              uncond_text_encoder_hidden_states,
          ) = self.text_encoder(
              input_ids=uncond_text_input_ids,
              attention_mask=uncond_text_mask,
          )
  
          # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
  
          seq_len = negative_prompt_embeds.shape[1]
          negative_prompt_embeds = negative_prompt_embeds.repeat(
              1, num_images_per_prompt
          )
          negative_prompt_embeds = negative_prompt_embeds.view(
              batch_size * num_images_per_prompt, seq_len
          )
  
          seq_len = uncond_text_encoder_hidden_states.shape[1]
          uncond_text_encoder_hidden_states = (
              uncond_text_encoder_hidden_states.repeat(
                  1, num_images_per_prompt, 1
              )
          )
          uncond_text_encoder_hidden_states = (
              uncond_text_encoder_hidden_states.view(
                  batch_size * num_images_per_prompt, seq_len, -1
              )
          )
          uncond_text_mask = uncond_text_mask.repeat_interleave(
              num_images_per_prompt, dim=0
          )
  
          # done duplicates
  
          # For classifier free guidance, we need to do two forward passes.
          # Here we concatenate the unconditional and text embeddings into a single batch
          # to avoid doing two forward passes
          prompt_embeds = torch.cat(
              [negative_prompt_embeds, prompt_embeds]
          )
          text_encoder_hidden_states = torch.cat(
              [
                  uncond_text_encoder_hidden_states,
                  text_encoder_hidden_states,
              ]
          )
  
          text_mask = torch.cat([uncond_text_mask, text_mask])
  
      return prompt_embeds, text_encoder_hidden_states, text_mask
  
file_path: diffusers/src/diffusers/pipelines/kandinsky/pipeline_kandinsky.py
imports: from typing import Callable, List, Optional, Union
  import torch
  from transformers import XLMRobertaTokenizer
  from ...models import UNet2DConditionModel, VQModel
  from ...schedulers import DDIMScheduler, DDPMScheduler
  from ...utils import logging, replace_example_docstring
  from ...utils.torch_utils import randn_tensor
  from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
  from .text_encoder import MultilingualCLIP
label_desc: Use image node_id 3 for calling the KandinskyPipeline obj's underlying member method code with example usage: obj._encode_prompt(prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt) and returns: prompt_embeds,  text_encoder_hidden_states,  text_mask