docstr: Configure checkpoint settings to write snapshots every epoch during the training process.
  In order to take effect, it needs to be called before fit.
  
  # Arguments
  path: The path to save snapshots. Make sure this path exists beforehand.
  over_write: Whether to overwrite existing snapshots in the given path. Default is True.
docstr_tok: ["Configure","checkpoint","settings","to","write","snapshots","every","epoch","during","the","training","process",".","In","order","to","take","effect",",","it","needs","to","be","called","before","fit",".","#","Arguments","path",":","The","path","to","save","snapshots",".","Make","sure","this","path","exists","beforehand",".","over_write",":","Whether","to","overwrite","existing","snapshots","in","the","given","path",".","Default","is","True","."]
code: def set_checkpoint(self, path, over_write=True):
      """
      Configure checkpoint settings to write snapshots every epoch during the training process.
      In order to take effect, it needs to be called before fit.
  
      # Arguments
      path: The path to save snapshots. Make sure this path exists beforehand.
      over_write: Whether to overwrite existing snapshots in the given path. Default is True.
      """
      callZooFunc(
          self.bigdl_type,
          "zooSetCheckpoint",
          self.value,
          path,
          over_write,
      )
  
imports: import zoo.pipeline.api.autograd
  from zoo.feature.image import ImageSet
  from zoo.feature.text import TextSet
  from zoo.feature.common import FeatureSet
  from zoo.pipeline.api.keras.base import ZooKerasLayer
  from zoo.pipeline.api.keras.utils import *
  from bigdl.nn.layer import Layer
  from zoo.common.utils import callZooFunc
label_desc: Use image node_id 7 for calling the KerasNet obj's underlying member method code with example usage: obj.set_checkpoint(path, over_write) without return types