code: def __call__(
      self,
      prompt_ids: jax.Array,
      params: Union[Dict, FrozenDict],
      prng_seed: jax.Array,
      num_inference_steps: int = 50,
      guidance_scale: Union[float, jax.Array] = 7.5,
      height: Optional[int] = None,
      width: Optional[int] = None,
      latents: jnp.array = None,
      neg_prompt_ids: jnp.array = None,
      return_dict: bool = True,
      output_type: str = None,
      jit: bool = False,
  ):
      # 0. Default height and width to unet
      height = (
          height or self.unet.config.sample_size * self.vae_scale_factor
      )
      width = (
          width or self.unet.config.sample_size * self.vae_scale_factor
      )
  
      if isinstance(guidance_scale, float) and jit:
          # Convert to a tensor so each device gets a copy.
          guidance_scale = jnp.array(
              [guidance_scale] * prompt_ids.shape[0]
          )
          guidance_scale = guidance_scale[:, None]
  
      return_latents = output_type == "latent"
  
      if jit:
          images = _p_generate(
              self,
              prompt_ids,
              params,
              prng_seed,
              num_inference_steps,
              height,
              width,
              guidance_scale,
              latents,
              neg_prompt_ids,
              return_latents,
          )
      else:
          images = self._generate(
              prompt_ids,
              params,
              prng_seed,
              num_inference_steps,
              height,
              width,
              guidance_scale,
              latents,
              neg_prompt_ids,
              return_latents,
          )
  
      if not return_dict:
          return (images,)
  
      return FlaxStableDiffusionXLPipelineOutput(images=images)
  
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 3 for calling the FlaxStableDiffusionXLPipeline obj's underlying member method code with example usage: obj.__call__(prompt_ids, params, prng_seed, num_inference_steps, guidance_scale, height, width, latents, neg_prompt_ids, return_dict, output_type, jit) and returns: FlaxStableDiffusionXLPipelineOutput, images