diff --git a/src/TensorFlowNET.Core/NumPy/Implementation/RandomizedImpl.cs b/src/TensorFlowNET.Core/NumPy/Implementation/RandomizedImpl.cs
index 244fc61b..bed327a3 100644
--- a/src/TensorFlowNET.Core/NumPy/Implementation/RandomizedImpl.cs
+++ b/src/TensorFlowNET.Core/NumPy/Implementation/RandomizedImpl.cs
@@ -23,7 +23,10 @@ namespace Tensorflow.NumPy
         public NDArray rand(params int[] shape)
             => throw new NotImplementedException("");
 
-        public NDArray randint(long x)
+        public NDArray randint(int low, int? high = null, Shape size = null, TF_DataType dtype = TF_DataType.TF_INT32)
+            => throw new NotImplementedException("");
+
+        public NDArray normal(float loc = 0.0f, float scale = 1.0f, Shape size = null)
             => throw new NotImplementedException("");
     }
 }
diff --git a/src/TensorFlowNET.Core/NumPy/Numpy.Manipulation.cs b/src/TensorFlowNET.Core/NumPy/Numpy.Manipulation.cs
index 2e970839..e75343db 100644
--- a/src/TensorFlowNET.Core/NumPy/Numpy.Manipulation.cs
+++ b/src/TensorFlowNET.Core/NumPy/Numpy.Manipulation.cs
@@ -8,10 +8,16 @@ namespace Tensorflow.NumPy
 {
     public partial class np
     {
+        [AutoNumPy]
+        public static NDArray expand_dims(NDArray a, Axis? axis = null) => throw new NotImplementedException("");
+
         [AutoNumPy]
         public static NDArray reshape(NDArray x1, Shape newshape) => x1.reshape(newshape);
 
         [AutoNumPy]
         public static NDArray squeeze(NDArray x1, Axis? axis = null) => new NDArray(array_ops.squeeze(x1, axis));
+
+        [AutoNumPy]
+        public static NDArray dstack(params NDArray[] tup) => throw new NotImplementedException("");
     }
 }
diff --git a/src/TensorFlowNET.Core/Numpy/Numpy.Creation.cs b/src/TensorFlowNET.Core/Numpy/Numpy.Creation.cs
index 9ade2725..c15cd685 100644
--- a/src/TensorFlowNET.Core/Numpy/Numpy.Creation.cs
+++ b/src/TensorFlowNET.Core/Numpy/Numpy.Creation.cs
@@ -35,7 +35,11 @@ namespace Tensorflow.NumPy
 
         [AutoNumPy]
         public static NDArray full<T>(Shape shape, T fill_value)
-            => new NDArray(tf.fill(tf.constant(shape), fill_value));
+            where T : unmanaged => new NDArray(tf.fill(tf.constant(shape), fill_value));
+
+        [AutoNumPy]
+        public static NDArray full_like<T>(NDArray x, T fill_value, TF_DataType? dtype = null, Shape shape = null)
+            where T : unmanaged => new NDArray(array_ops.fill(x.shape, constant_op.constant(fill_value)));
 
         [AutoNumPy]
         public static NDArray frombuffer(byte[] bytes, Shape shape, TF_DataType dtype)
diff --git a/src/TensorFlowNET.Keras/Preprocessings/DatasetUtils.index_directory.cs b/src/TensorFlowNET.Keras/Preprocessings/DatasetUtils.index_directory.cs
index cfcbc4bc..1eb4f431 100644
--- a/src/TensorFlowNET.Keras/Preprocessings/DatasetUtils.index_directory.cs
+++ b/src/TensorFlowNET.Keras/Preprocessings/DatasetUtils.index_directory.cs
@@ -49,7 +49,7 @@ namespace Tensorflow.Keras.Preprocessings
             if (shuffle)
             {
                 if (!seed.HasValue)
-                    seed = np.random.randint((long)1e6);
+                    seed = np.random.randint((int)1e6);
                 var random_index = np.arange(label_list.Count);
                 tf.set_random_seed(seed.Value);
                 np.random.shuffle(random_index);