docstr: If `learning_rates='auto'` then a very wide range of learning rates
  will be added. Otherwise a list with one or more learning rates
  is used.
  
  NOTE: talos.utils.lr_normalizer should be used if more than one optimizer
  is used in the experiment
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code: def lr(self, learning_rates="auto"):
      """If `learning_rates='auto'` then a very wide range of learning rates
      will be added. Otherwise a list with one or more learning rates
      is used.
  
      NOTE: talos.utils.lr_normalizer should be used if more than one optimizer
      is used in the experiment
      """
  
      if learning_rates == "auto":
          a = np.round(np.arange(0.01, 0.2, 0.02), 3).tolist()
          b = np.round(np.arange(0, 1, 0.2), 2).tolist()
          c = list(range(0, 11))
  
          self._append_params("lr", a + b + c)
  
      else:
          self._append_params("lr", learning_rates)
  
imports: import numpy
  from tensorflow.keras.optimizers import Adam, Nadam, Adadelta, SGD
label_desc: Use image node_id 14 for calling the AutoParams obj's underlying member method code with example usage: obj.lr(learning_rates) without return types