docstr: A facility for generating or appending params dictionary.
  
  params : dict or None
  task : str
       'binary', 'multi_class', 'multi_label', or 'continuous'
  replace : bool
       Replace current dictionary entries with new ones.
  auto : bool
       Automatically generate or append params dictionary with
       all available parameters.
  network : bool
       Adds several network architectures as parameters. This is to be
       used as an input together with KerasModel(). If False then only
       'dense' will be added.
  resample_params | int or False | The number of values per parameter
docstr_tok: ["A","facility","for","generating","or","appending","params","dictionary",".","params",":","dict","or","None","task",":","str","'binary","'",",","'multi_class","'",",","'multi_label","'",",","or","'continuous'","replace",":","bool","Replace","current","dictionary","entries","with","new","ones",".","auto",":","bool","Automatically","generate","or","append","params","dictionary","with","all","available","parameters",".","network",":","bool","Adds","several","network","architectures","as","parameters",".","This","is","to","be","used","as","an","input","together","with","KerasModel","(",")",".","If","False","then","only","'dense","'","will","be","added",".","resample_params","|","int","or","False","|","The","number","of","values","per","parameter"]
code: def __init__(
      self,
      params=None,
      task="binary",
      replace=True,
      auto=True,
      network=True,
      resample_params=4,
  ):
      """A facility for generating or appending params dictionary.
  
      params : dict or None
      task : str
           'binary', 'multi_class', 'multi_label', or 'continuous'
      replace : bool
           Replace current dictionary entries with new ones.
      auto : bool
           Automatically generate or append params dictionary with
           all available parameters.
      network : bool
           Adds several network architectures as parameters. This is to be
           used as an input together with KerasModel(). If False then only
           'dense' will be added.
      resample_params | int or False | The number of values per parameter
      """
  
      self._task = task
      self._replace = replace
      self._network = network
  
      if params is None:
          self.params = {}
      else:
          self.params = params
      if auto:
          self._automated()
  
      if resample_params is not False:
          self.resample_params(resample_params)
  
imports: import numpy
  from tensorflow.keras.optimizers import Adam, Nadam, Adadelta, SGD
label_desc: Use image node_id 1 to create a new AutoParams object  with example: obj = AutoParams(params, task, replace, auto, network, resample_params)