class_docstr: Wrapper utility class that allows models to used in scikit-learn's
  ``GridSearchCV`` API. It follows the style of Keras' own wrapper.
  
  A future release of **baikal** plans to remove this class and instead
  include a custom ``GridSearchCV`` API, based on the original scikit-learn
  implementation, that can handle baikal models natively.
  
  Parameters
  ----------
  build_fn
      A function that takes no arguments and builds and returns a baikal Model.
  
      Note that, in order to specify which parameters of which steps to tune
      using a dictionary keyed by ``<step>__<parameter>``, you *must* pass a
      name to the appropriate steps when building the model in this function.
  
  params
      Dictionary mapping parameter names to their values. Valid parameter
      names are 'build_fn' and any parameter the wrapped model can take
      (in the form ``<step>__<parameter>``).
code: def __init__(self, build_fn: Callable[[], Model], **params):
      # build_fn must be a function that builds and returns a baikal Model
      self.build_fn = build_fn
      self._model = build_fn()
      self.set_params(**params)
  
file_path: baikal/baikal/sklearn.py
imports: from typing import Dict, Any, Callable
  from baikal import Model
label_desc: Use image node_id 1 to create a new SKLearnWrapper object  with example: obj = SKLearnWrapper(build_fn)