class_docstr: LIT model wrapper for the Dalle-Mini Text-to-Image model.
  
  The model wrapper consists of a few already known works connected in an
  interesting way to generate images from the text:
  
  * VQGAN: The generative image model.
  * BART: A sequence-to-sequence autoencoder used to reconstruct input text.
  * CLIP: A scoring model that assesses the alingment of an image and a prompt.
  
  The basic flow within this model wrapper's predict() function is:
  
  1.  The BART encoder is fed the prompt text.
  2.  The BART decoder is sampled multiple times to generate candidates.
  3.  Each candidate is passed to VQGAN, which generates images.
  4.  CLIP then scores each generated image against the prompt.
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code: def max_minibatch_size(self) -> int:
      return 8
  
imports: from collections.abc import Iterable
  import functools
  import random
  from typing import Optional
  import dalle_mini
  import flax
  from flax.training import common_utils
  import jax
  from jax import numpy
  from lit_nlp.api import model
  from lit_nlp.api import types
  from lit_nlp.lib import image_utils
  import numpy
  from PIL import Image
  import tqdm.notebook
  import transformers
  from vqgan_jax import modeling_flax_vqgan
label_desc: Use image node_id 2 for calling the DalleModel obj's underlying member method code with example usage: obj.max_minibatch_size() and returns: int