import tensorflow as tf


		if hparams.wavenet_test_size is None:
			assert hparams.wavenet_test_batches == self.test_steps

		#Get conditioning status
		self.local_condition, self.global_condition = self._check_conditions()

		with tf.device('/cpu:0'):
			# Create placeholders for inputs and targets. Don't specify batch size because we want
			# to be able to feed different batch sizes at eval time.
			if is_scalar_input(hparams.input_type):
				input_placeholder = tf.placeholder(tf.float32, shape=(None, 1, None), name='audio_inputs')
				target_placeholder = tf.placeholder(tf.float32, shape=(None, None, 1), name='audio_targets')
				target_type = tf.float32
			else:
				input_placeholder = tf.placeholder(tf.float32, shape=(None, hparams.quantize_channels, None), name='audio_inputs')
				target_placeholder = tf.placeholder(tf.int32, shape=(None, None, 1), name='audio_targets')
				target_type = tf.int32

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