class_docstr: ```
  Abstract class used to tune and train Keras models. The fit method is
  an abstract method and must be implemented by subclasses.
  ```
class_docstr_tok: ["``","`","Abstract","class","used","to","tune","and","train","Keras","models",".","The","fit","method","is","an","abstract","method","and","must","be","implemented","by","subclasses",".","``","`"]
docstr: ```
  prints the layers of the model along with indices
  ```
docstr_tok: ["``","`","prints","the","layers","of","the","model","along","with","indices","``","`"]
code: def print_layers(self, show_wd=False):
      """
      ```
      prints the layers of the model along with indices
      ```
      """
      if show_wd:
          warnings.warn(
              "set_weight_decay now uses AdamWeightDecay instead of kernel_regularizers."
          )
      for i, layer in enumerate(self.model.layers):
          if show_wd and hasattr(layer, "kernel_regularizer"):
              reg = layer.kernel_regularizer
              if hasattr(reg, "l2"):
                  wd = reg.l2
              elif hasattr(reg, "l1"):
                  wd = reg.l1
              else:
                  wd = None
              print(
                  "%s (trainable=%s, wd=%s) : %s"
                  % (i, layer.trainable, wd, layer)
              )
          else:
              print(
                  "%s (trainable=%s) : %s" % (i, layer.trainable, layer)
              )
      return
  
imports: from .None import utils
  from .graph.predictor import LinkPredictor, NodePredictor
  from .graph.preprocessor import LinkPreprocessor, NodePreprocessor
  from .imports import *
  from .lroptimize.lrfinder import *
  from .tabular.predictor import TabularPredictor
  from .tabular.preprocessor import TabularPreprocessor
  from .text.ner.predictor import NERPredictor
  from .text.ner.preprocessor import NERPreprocessor
  from .text.predictor import TextPredictor
  from .text.preprocessor import BERTPreprocessor, TextPreprocessor, TransformersPreprocessor
  from .vision.predictor import ImagePredictor
  from .vision.preprocessor import ImagePreprocessor
label_desc: Use image node_id 23 for calling the Learner obj's underlying member method code with example usage: obj.print_layers(show_wd) without return types