class_docstr: Pipeline for upscaling Stable Diffusion output image resolution by a factor of 2.
  
  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.).
  
  The pipeline also inherits the following loading methods:
      - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files
  
  Args:
      vae ([`AutoencoderKL`]):
          Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
      text_encoder ([`~transformers.CLIPTextModel`]):
          Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
      tokenizer ([`~transformers.CLIPTokenizer`]):
          A `CLIPTokenizer` to tokenize text.
      unet ([`UNet2DConditionModel`]):
          A `UNet2DConditionModel` to denoise the encoded image latents.
      scheduler ([`SchedulerMixin`]):
          A [`EulerDiscreteScheduler`] to be used in combination with `unet` to denoise the encoded image latents.
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docstr: Encodes the prompt into text encoder hidden states.
  
  Args:
      prompt (`str` or `list(int)`):
          prompt to be encoded
      device: (`torch.device`):
          torch device
      do_classifier_free_guidance (`bool`):
          whether to use classifier free guidance or not
      negative_prompt (`str` or `List[str]`):
          The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
          if `guidance_scale` is less than `1`).
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code: def _encode_prompt(
      self, prompt, device, do_classifier_free_guidance, negative_prompt
  ):
      r"""
      Encodes the prompt into text encoder hidden states.
  
      Args:
          prompt (`str` or `list(int)`):
              prompt to be encoded
          device: (`torch.device`):
              torch device
          do_classifier_free_guidance (`bool`):
              whether to use classifier free guidance or not
          negative_prompt (`str` or `List[str]`):
              The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
              if `guidance_scale` is less than `1`).
      """
      batch_size = len(prompt) if isinstance(prompt, list) else 1
  
      text_inputs = self.tokenizer(
          prompt,
          padding="max_length",
          max_length=self.tokenizer.model_max_length,
          truncation=True,
          return_length=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_encoder_out = self.text_encoder(
          text_input_ids.to(device),
          output_hidden_states=True,
      )
      text_embeddings = text_encoder_out.hidden_states[-1]
      text_pooler_out = text_encoder_out.pooler_output
  
      # get unconditional embeddings for classifier free guidance
      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
  
          max_length = text_input_ids.shape[-1]
          uncond_input = self.tokenizer(
              uncond_tokens,
              padding="max_length",
              max_length=max_length,
              truncation=True,
              return_length=True,
              return_tensors="pt",
          )
  
          uncond_encoder_out = self.text_encoder(
              uncond_input.input_ids.to(device),
              output_hidden_states=True,
          )
  
          uncond_embeddings = uncond_encoder_out.hidden_states[-1]
          uncond_pooler_out = uncond_encoder_out.pooler_output
  
          # 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
          text_embeddings = torch.cat(
              [uncond_embeddings, text_embeddings]
          )
          text_pooler_out = torch.cat(
              [uncond_pooler_out, text_pooler_out]
          )
  
      return text_embeddings, text_pooler_out
  
imports: import warnings
  from typing import Callable, List, Optional, Union
  import numpy
  import PIL.Image
  import torch
  import torch.nn.functional
  from transformers import CLIPTextModel, CLIPTokenizer
  from ...image_processor import PipelineImageInput, VaeImageProcessor
  from ...loaders import FromSingleFileMixin
  from ...models import AutoencoderKL, UNet2DConditionModel
  from ...schedulers import EulerDiscreteScheduler
  from ...utils import deprecate, logging
  from ...utils.torch_utils import randn_tensor
  from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
label_desc: Use image node_id 2 for calling the StableDiffusionLatentUpscalePipeline obj's underlying member method code with example usage: obj._encode_prompt(prompt, device, do_classifier_free_guidance, negative_prompt) and returns: text_embeddings,  text_pooler_out