class_docstr: Base class for Flax-based pipelines.
  
  [`FlaxDiffusionPipeline`] stores all components (models, schedulers, and processors) for diffusion pipelines and
  provides methods for loading, downloading and saving models. It also includes methods to:
  
      - enable/disable the progress bar for the denoising iteration
  
  Class attributes:
  
      - **config_name** ([`str`]) -- The configuration filename that stores the class and module names of all the
        diffusion pipeline's components.
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code: def progress_bar(self, iterable):
      if not hasattr(self, "_progress_bar_config"):
          self._progress_bar_config = {}
      elif not isinstance(self._progress_bar_config, dict):
          raise ValueError(
              f"`self._progress_bar_config` should be of type `dict`, but is {type(self._progress_bar_config)}."
          )
  
      return tqdm(iterable, **self._progress_bar_config)
  
imports: import importlib
  import inspect
  import os
  from typing import Any, Dict, List, Optional, Union
  import flax
  import numpy
  import PIL.Image
  from flax.core.frozen_dict import FrozenDict
  from huggingface_hub import create_repo, snapshot_download
  from huggingface_hub.utils import validate_hf_hub_args
  from PIL import Image
  from tqdm.auto import tqdm
  from ..configuration_utils import ConfigMixin
  from ..models.modeling_flax_utils import FLAX_WEIGHTS_NAME, FlaxModelMixin
  from ..schedulers.scheduling_utils_flax import SCHEDULER_CONFIG_NAME, FlaxSchedulerMixin
  from ..utils import CONFIG_NAME, BaseOutput, PushToHubMixin, http_user_agent, is_transformers_available, logging
label_desc: Use image node_id 7 for calling the FlaxDiffusionPipeline obj's underlying member method code with example usage: obj.progress_bar(iterable) and returns: tqdm