diff --git a/utils/data.py b/utils/data.py
index 4ff3a4c4..a38092e9 100644
--- a/utils/data.py
+++ b/utils/data.py
@@ -1,7 +1,7 @@
 import numpy as np
 
 
-def _pad_data(x, length):
+def pad_data(x, length):
     _pad = 0
     assert x.ndim == 1
     return np.pad(x, (0, length - x.shape[0]),
@@ -11,31 +11,7 @@ def _pad_data(x, length):
 
 def prepare_data(inputs):
     max_len = max((len(x) for x in inputs))
-    return np.stack([_pad_data(x, max_len) for x in inputs])
-
-
-def _pad_tensor(x, length):
-    _pad = 0
-    assert x.ndim == 2
-    x = np.pad(x, [[0, 0], [0, length - x.shape[1]]], mode='constant', constant_values=_pad)
-    return x
-
-def prepare_tensor(inputs, out_steps):
-    max_len = max((x.shape[1] for x in inputs)) + 1 # zero-frame
-    remainder = max_len % out_steps
-    return np.stack([_pad_tensor(x, max_len + (out_steps - remainder)) for x in inputs])
-
-
-def _pad_stop_target(x, length):
-    _pad = 1.
-    assert x.ndim == 1
-    return np.pad(x, (0, length - x.shape[0]), mode='constant', constant_values=_pad)
-
-
-def prepare_stop_target(inputs, out_steps):
-    max_len = max((x.shape[0] for x in inputs)) + 1  # zero-frame
-    remainder = max_len % out_steps
-    return np.stack([_pad_stop_target(x, max_len + (out_steps - remainder)) for x in inputs])
+    return np.stack([pad_data(x, max_len) for x in inputs])
 
 
 def pad_per_step(inputs, pad_len):