code: def get_embeddings(self, prompt_ids: jnp.array, params):
      # We assume we have the two encoders
  
      # bs, encoder_input, seq_length
      te_1_inputs = prompt_ids[:, 0, :]
      te_2_inputs = prompt_ids[:, 1, :]
  
      prompt_embeds = self.text_encoder(
          te_1_inputs,
          params=params["text_encoder"],
          output_hidden_states=True,
      )
      prompt_embeds = prompt_embeds["hidden_states"][-2]
      prompt_embeds_2_out = self.text_encoder_2(
          te_2_inputs,
          params=params["text_encoder_2"],
          output_hidden_states=True,
      )
      prompt_embeds_2 = prompt_embeds_2_out["hidden_states"][-2]
      text_embeds = prompt_embeds_2_out["text_embeds"]
      prompt_embeds = jnp.concatenate(
          [prompt_embeds, prompt_embeds_2], axis=-1
      )
      return prompt_embeds, text_embeds
  
file_path: llmga/llmga/diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_flax_stable_diffusion_xl.py
imports: from functools import partial
  from typing import Dict, List, Optional, Union
  import jax
  import jax.numpy
  from flax.core.frozen_dict import FrozenDict
  from transformers import CLIPTokenizer, FlaxCLIPTextModel
  from diffusers.utils import logging
  from ...models import FlaxAutoencoderKL, FlaxUNet2DConditionModel
  from ...schedulers import FlaxDDIMScheduler, FlaxDPMSolverMultistepScheduler, FlaxLMSDiscreteScheduler, FlaxPNDMScheduler
  from ..pipeline_flax_utils import FlaxDiffusionPipeline
  from .pipeline_output import FlaxStableDiffusionXLPipelineOutput
label_desc: Use image node_id 4 for calling the FlaxStableDiffusionXLPipeline obj's underlying member method code with example usage: obj.get_embeddings(prompt_ids, params) and returns: prompt_embeds,  text_embeds