one read_and_decode function for different training data - tensorflow

Question

I'm new to TensorFlow, and here's what I'm trying to do: save training data from different scenarios and then read them back. The sizes of feature and output may be different for different scenarios.
The issue is when I tried to read the data back, I got an exception that looks like the following:
InvalidArgumentError (see above for traceback): Name: <unknown>, Key: observation, Index: 0. Number of float values != expected. Values size: 17 but output shape: []
The function for saving data looks like below:
def save_data(obs, actions, filename):
writer = tf.python_io.TFRecordWriter(filename)
for index in range(num_examples):
o = obs[index].tolist()
a = actions[index].tolist()
example = tf.train.Example(features=tf.train.Features(
feature = {
'obs' : tf.train.Feature(float_list=tf.train.FloatList(value=o)),
'action': tf.train.Feature(float_list=tf.train.FloatList(value=a)),
'obs_size' : tf.train.Feature(int64_list=tf.train.Int64List(value=[len(o)])),
'action_size': tf.train.Feature(int64_list=tf.train.Feature(int64_list=tf.train.Int64List(value=[len(a)])),
}
))
writer.write(example.SerializeToString())
writer.close()
The function to read the data back is as follows:
def read_and_decode(filename_queue):
reader = tf.TFRecordReader()
_, example = reader.read(filename_queue)
features = tf.parse_single_example(
example,
features = {
'obs' : tf.FixedLenFeature([], tf.float32),
'action' : tf.FixedLenFeature([], tf.float32),
'obs_size': tf.FixedLenFeature([], tf.int64),
'action_size' : tf.FixedLenFeature([], tf.int64)
}
)
obs_size = tf.cast(features['observation_size'], tf.int32)
action_size = tf.cast(features['action_size'], tf.int32)
obs_shape = tf.pack([1, obs_size])
action_shape = tf.pack([1, action_size])
obs = tf.reshape(obs, obs_shape)
action = tf.reshape(action, action_shape)