import tensorflow as tf


			self._placeholders = [
			input_placeholder,
			target_placeholder,
			tf.placeholder(tf.int32, shape=(None, ), name='input_lengths'),
			]

			queue_types = [tf.float32, target_type, tf.int32]

			if self.local_condition:
				self._placeholders.append(tf.placeholder(tf.float32, shape=(None, hparams.num_mels, None), name='local_condition_features'))
				queue_types.append(tf.float32)
			if self.global_condition:
				self._placeholders.append(tf.placeholder(tf.int32, shape=(None, 1), name='global_condition_features'))
				queue_types.append(tf.int32)

			# Create queue for buffering data
			queue = tf.FIFOQueue(8, queue_types, name='input_queue')
			self._enqueue_op = queue.enqueue(self._placeholders)
			variables = queue.dequeue()

			self.inputs = variables[0]
			self.inputs.set_shape(self._placeholders[0].shape)
			self.targets = variables[1]
			self.targets.set_shape(self._placeholders[1].shape)
			self.input_lengths = variables[2]
