diff --git a/src/TensorFlowNET.Core/Operations/map_fn.cs b/src/TensorFlowNET.Core/Operations/map_fn.cs
new file mode 100644
index 00000000..1206d5b9
--- /dev/null
+++ b/src/TensorFlowNET.Core/Operations/map_fn.cs
@@ -0,0 +1,86 @@
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using System.Text;
+using Tensorflow.Operations;
+using static Tensorflow.Binding;
+
+namespace Tensorflow
+{
+    public partial class Operation
+    {
+        /// <summary>
+        /// map on the list of tensors unpacked from `elems` on dimension 0.
+        /// </summary>
+        /// <param name="fn"></param>
+        /// <param name="elems"></param>
+        /// <param name="dtype"></param>
+        /// <param name="parallel_iterations"></param>
+        /// <param name="back_prop"></param>
+        /// <param name="swap_memory"></param>
+        /// <param name="infer_shape"></param>
+        /// <param name="name"></param>
+        /// <returns>A tensor or (possibly nested) sequence of tensors.</returns>
+        public static Tensor map_fn(Func<Tensor, Tensor> fn, 
+            Tensor elems,
+            TF_DataType dtype = TF_DataType.DtInvalid,
+            int parallel_iterations = 10,
+            bool back_prop = true,
+            bool swap_memory = false,
+            bool infer_shape = true,
+            string name = null)
+        {
+            var elems_flat = new[] { elems };
+            tf_with(ops.name_scope(name, "map", elems_flat), delegate
+            {
+                var varscope = tf.get_variable_scope();
+                elems_flat = elems_flat.Select(elem => ops.convert_to_tensor(elem, name: "elem"))
+                    .ToArray();
+
+                dtype = elems_flat.Select(elem => elem.dtype).First();
+                var dtype_flat = new[] { dtype };
+
+                // Convert elems to tensor array. n may be known statically.
+                var static_shape = elems_flat[0].shape;
+
+                var n = static_shape[0];
+
+                // TensorArrays are always flat
+                var elems_ta = elems_flat.Select(elem => new TensorArray(dtype: elem.dtype,
+                        size: ops.convert_to_tensor(n),
+                        dynamic_size: false,
+                        infer_shape: true)).ToArray();
+
+                // Unpack elements
+                var elems_ta_1 = new List<TensorArray>();
+                foreach (var (elem_ta, elem) in zip(elems_ta, elems_flat))
+                    elems_ta_1.Add(elem_ta.unstack(elem));
+
+                elems_ta = elems_ta_1.ToArray();
+
+                var i = constant_op.constant(0);
+
+                var accs_ta = dtype_flat.Select(dt => new TensorArray(dtype: dt,
+                        size: ops.convert_to_tensor(n),
+                        dynamic_size: false,
+                        infer_shape: infer_shape)).ToArray();
+
+                /*Func<Tensor, TensorArray> compute = (i, tas) =>
+                {
+                    throw new NotImplementedException("");
+                };
+
+                var r_a = control_flow_ops.while_loop(
+                    (i, _) => i < n, 
+                    compute, 
+                    new[] { i, accs_ta },
+                    parallel_iterations: parallel_iterations,
+                    back_prop: back_prop,
+                    swap_memory: swap_memory,
+                    maximum_iterations: n);*/
+            });
+
+            throw new NotImplementedException("");
+        }
+    }
+}