docstr: Automatically generate a comprehensive
  parameter dict to be used in Scan()
  
  shapes : string
      Either 'fixed' or 'sloped'
docstr_tok: ["Automatically","generate","a","comprehensive","parameter","dict","to","be","used","in","Scan","(",")","shapes",":","string","Either","'fixed","'","or","'sloped","'"]
code: def _automated(self, shapes="fixed"):
      """Automatically generate a comprehensive
      parameter dict to be used in Scan()
  
      shapes : string
          Either 'fixed' or 'sloped'
  
      """
  
      if shapes == "fixed":
          self.shapes()
      else:
          self.shapes_slope()
      self.layers()
      self.dropout()
      self.optimizers()
      self.activations()
      self.neurons()
      self.losses()
      self.batch_size()
      self.epochs()
      self.kernel_initializers()
      self.lr()
      if self._network:
          self.networks()
      else:
          self.params["network"] = ["dense"]
      self.last_activations()
  
imports: import numpy
  from tensorflow.keras.optimizers import Adam, Nadam, Adadelta, SGD
label_desc: Use image node_id 2 for calling the AutoParams obj's underlying member method code with example usage: obj._automated(shapes) without return types