code: def fit(
      n=10000,
      method="advi",
      model=None,
      random_seed=None,
      start=None,
      start_sigma=None,
      inf_kwargs=None,
      **kwargs,
  ):
      r"""Handy shortcut for using inference methods in functional way
  
      Parameters
      ----------
      n: `int`
          number of iterations
      method: str or :class:`Inference`
          string name is case insensitive in:
  
          -   'advi'  for ADVI
          -   'fullrank_advi'  for FullRankADVI
          -   'svgd'  for Stein Variational Gradient Descent
          -   'asvgd'  for Amortized Stein Variational Gradient Descent
  
      model: :class:`Model`
          PyMC model for inference
      random_seed: None or int
      inf_kwargs: dict
          additional kwargs passed to :class:`Inference`
      start: `dict[str, np.ndarray]` or `StartDict`
          starting point for inference
      start_sigma: `dict[str, np.ndarray]`
          starting standard deviation for inference, only available for method 'advi'
  
      Other Parameters
      ----------------
      score: bool
              evaluate loss on each iteration or not
      callbacks: list[function: (Approximation, losses, i) -> None]
          calls provided functions after each iteration step
      progressbar: bool
          whether to show progressbar or not
      obj_n_mc: `int`
          Number of monte carlo samples used for approximation of objective gradients
      tf_n_mc: `int`
          Number of monte carlo samples used for approximation of test function gradients
      obj_optimizer: function (grads, params) -> updates
          Optimizer that is used for objective params
      test_optimizer: function (grads, params) -> updates
          Optimizer that is used for test function params
      more_obj_params: `list`
          Add custom params for objective optimizer
      more_tf_params: `list`
          Add custom params for test function optimizer
      more_updates: `dict`
          Add custom updates to resulting updates
      total_grad_norm_constraint: `float`
          Bounds gradient norm, prevents exploding gradient problem
      fn_kwargs: `dict`
          Add kwargs to pytensor.function (e.g. `{'profile': True}`)
      more_replacements: `dict`
          Apply custom replacements before calculating gradients
  
      Returns
      -------
      :class:`Approximation`
      """
      if inf_kwargs is None:
          inf_kwargs = dict()
      else:
          inf_kwargs = inf_kwargs.copy()
      if random_seed is not None:
          inf_kwargs["random_seed"] = random_seed
      if start is not None:
          inf_kwargs["start"] = start
      if start_sigma is not None:
          if method != "advi":
              raise NotImplementedError(
                  "start_sigma is only available for method advi"
              )
          inf_kwargs["start_sigma"] = start_sigma
      if model is None:
          model = pm.modelcontext(model)
      _select = dict(
          advi=ADVI, fullrank_advi=FullRankADVI, svgd=SVGD, asvgd=ASVGD
      )
      if isinstance(method, str):
          method = method.lower()
          if method in _select:
              inference = _select[method](model=model, **inf_kwargs)
          else:
              raise KeyError(
                  f"method should be one of {set(_select.keys())} or Inference instance"
              )
      elif isinstance(method, Inference):
          inference = method
      else:
          raise TypeError(
              f"method should be one of {set(_select.keys())} or Inference instance"
          )
      return inference.fit(n, **kwargs)
  
imports: import collections
  import logging
  import warnings
  import numpy
  from fastprogress.fastprogress import progress_bar
  import pymc
  from pymc.variational import test_functions
  from pymc.variational.approximations import Empirical, FullRank, MeanField
  from pymc.variational.operators import KL, KSD
label_desc: Use image node_id 1 for calling a global function with example usage: fit(n, method, model, random_seed, start, start_sigma, inf_kwargs) and returns: inference