class_docstr: Interface is Gradio's main high-level class, and allows you to create a web-based GUI / demo
  around a machine learning model (or any Python function) in a few lines of code.
  You must specify three parameters: (1) the function to create a GUI for (2) the desired input components and
  (3) the desired output components. Additional parameters can be used to control the appearance
  and behavior of the demo.
  
  Example:
      import gradio as gr
  
      def image_classifier(inp):
          return {'cat': 0.3, 'dog': 0.7}
  
      demo = gr.Interface(fn=image_classifier, inputs="image", outputs="label")
      demo.launch()
  Demos: hello_world, hello_world_3, gpt2_xl
  Guides: quickstart, key-features, sharing-your-app, interface-state, reactive-interfaces, advanced-interface-features, setting-up-a-gradio-demo-for-maximum-performance
docstr: :return: list of all current instances.
code: def get_instances(cls) -> list[Interface]:
      """
      :return: list of all current instances.
      """
      return list(Interface.instances)
  
imports: from __future__ import annotations
  import inspect
  import json
  import os
  import warnings
  import weakref
  from typing import TYPE_CHECKING, Any, Callable, Literal
  from gradio_client.documentation import document, set_documentation_group
  from gradio import Examples, utils
  from gradio.blocks import Blocks
  from gradio.components import Button, ClearButton, Component, DuplicateButton, Markdown, State, get_component_instance
  from gradio.data_classes import InterfaceTypes
  from gradio.events import Events, on
  from gradio.exceptions import RenderError
  from gradio.flagging import CSVLogger, FlaggingCallback, FlagMethod
  from gradio.layouts import Accordion, Column, Row, Tab, Tabs
  from gradio.pipelines import load_from_pipeline
  from gradio.themes import ThemeClass
label_desc: Use image node_id 1 for calling the Interface obj's underlying member method code with example usage: obj.get_instances(cls) and returns: list