code: def test_tfdataset_with_tf_data_dataset_which_contains_bool(self):
      dataset = tf.data.Dataset.from_tensor_slices(
          (
              np.random.randn(102, 28, 28, 1),
              np.random.randint(0, 10, size=(102,)),
              np.ones(shape=(102, 28, 28, 1), dtype=np.bool),
          )
      )
      dataset = TFDataset.from_tf_data_dataset(dataset, batch_size=16)
  
      feature, labels, mask = dataset.tensors
  
      float_mask = tf.to_float(mask)
      masked_feature = tf.to_float(feature) * float_mask
      flatten = tf.layers.flatten(masked_feature)
      logits = tf.layers.dense(flatten, 10)
      loss = tf.reduce_mean(
          tf.losses.sparse_softmax_cross_entropy(
              logits=logits, labels=labels
          )
      )
      opt = TFOptimizer.from_loss(loss, Adam())
      opt.optimize()
  
imports: import pytest
  from pyspark.ml.linalg import DenseVector
  from zoo.feature.common import ChainedPreprocessing, FeatureSet
  from zoo.feature.image import *
  from test.zoo.pipeline.utils.test_utils import ZooTestCase
  from zoo.pipeline.api.keras.optimizers import Adam
  from zoo.tfpark import TFNet, TFOptimizer
  import tensorflow
  import numpy
  import os
  from zoo.tfpark import KerasModel, TFDataset
label_desc: Use image node_id 17 for calling the TestTFDataset obj's underlying member method code with example usage: obj.test_tfdataset_with_tf_data_dataset_which_contains_bool() without return types