message: [arrow] using eager exec for examples in README.md
diff: @@ -20,12 +20,11 @@ import tensorflow_io.arrow as arrow_io
  # Assume `df` is an existing Pandas DataFrame
  dataset = arrow_io.ArrowDataset.from_pandas(df)
  -iterator = dataset.make_one_shot_iterator()
  -next_element = iterator.get_next()
  +# All `tf.data.Dataset` operations can now be performed, for ex:
  +dataset = dataset.batch(2)
  -with tf.Session() as sess:
  - for i in range(len(df)):
  - print(sess.run(next_element))
  +for row in dataset:
  + print(row)
  ```
  NOTE: The entire DataFrame will be serialized to the Dataset and is not
  @@ -59,16 +58,9 @@ dataset = arrow_io.ArrowFeatherDataset(
  output_types=(tf.int32, tf.float32),
  output_shapes=([], []))
  -iterator = dataset.make_one_shot_iterator()
  -next_element = iterator.get_next()
  -
  # This will iterate over each row of each file provided
  -with tf.Session() as sess:
  - while True:
  - try:
  - print(sess.run(next_element))
  - except tf.errors.OutOfRangeError:
  - break
  +for row in dataset:
  + print(row)
  ```
  An alternate constructor can also be used to infer output types and shapes from
  @@ -101,17 +93,10 @@ dataset = arrow_io.ArrowStreamDataset(
  output_types=(tf.int32, tf.float32),
  output_shapes=([], []))
  -iterator = dataset.make_one_shot_iterator()
  -next_element = iterator.get_next()
  -
  # The host connection is made when the Dataset op is run and will iterate over
  # each row of each record batch until the Arrow stream is finished
  -with tf.Session() as sess:
  - while True:
  - try:
  - print(sess.run(next_element))
  - except tf.errors.OutOfRangeError:
  - break
  +for row in dataset:
  + print(row)
  ```
  An alternate constructor can also be used to infer output types and shapes from
  