class_docstr: dnn MNIST
class_docstr_tok: ["dnn","MNIST"]
code: def train(self, X, Y, testX, testY, n_epoch=1, snapshot_step=1000):
      # Training
      self.model.fit(
          X,
          Y,
          n_epoch=n_epoch,
          validation_set=(testX, testY),
          snapshot_step=snapshot_step,
          show_metric=True,
          run_id="dense_model",
      )
  
imports: from __future__ import division, print_function, absolute_import
  import re
  import tflearn
  import tensorflow.compat.v1
  import tflearn.datasets.mnist
  from tflearn.layers.core import input_data, dropout, fully_connected
  from tflearn.layers.conv import conv_2d, max_pool_2d
  from tflearn.layers.normalization import local_response_normalization
  from tflearn.layers.estimator import regression
label_desc: Use image node_id 3 for calling the Model2 obj's underlying member method code with example usage: obj.train(X, Y, testX, testY, n_epoch, snapshot_step) without return types