diff --git a/src/qibotn/cutn.py b/src/qibotn/cutn.py
index 3d42eb7..5267bc0 100644
--- a/src/qibotn/cutn.py
+++ b/src/qibotn/cutn.py
@@ -1,7 +1,6 @@
 from qibotn.QiboCircuitConvertor import QiboCircuitToEinsum
 from cuquantum import contract
 from cuquantum import cutensornet as cutn
-from mpi4py import MPI  # this line initializes MPI
 import multiprocessing
 from cupy.cuda.runtime import getDeviceCount
 import cupy as cp
@@ -12,9 +11,16 @@ def eval(qibo_circ, datatype):
     return contract(*myconvertor.state_vector_operands())
 
 
-def eval_tn_MPI(qibo_circ, datatype):
+def eval_tn_MPI(qibo_circ, datatype, n_samples=8):
+    """Convert qibo circuit to tensornet (TN) format and perform contraction using multi node and multi GPU through MPI.
+    The conversion is performed by QiboCircuitToEinsum() afterwhich it goes through 2 steps: pathfinder and execution.
+    The pathfinder looks at user defined number of samples (n_samples) iteratively to select the least costly contraction path. This is sped up with multi thread.
+    After pathfinding the optimal path is used in the actual contraction to give a dense vector representation of the TN.
+    """
+
+    from mpi4py import MPI  # this line initializes MPI
+
     ncpu_threads = multiprocessing.cpu_count() // 2
-    n_samples = 8
 
     comm = MPI.COMM_WORLD
     rank = comm.Get_rank()
@@ -25,14 +31,15 @@ def eval_tn_MPI(qibo_circ, datatype):
     cutn.distributed_reset_configuration(handle, *cutn.get_mpi_comm_pointer(comm))
     network_opts = cutn.NetworkOptions(handle=handle, blocking="auto")
 
+    # Perform circuit conversion
     myconvertor = QiboCircuitToEinsum(qibo_circ, dtype=datatype)
     operands_interleave = myconvertor.state_vector_operands()
 
+    # Pathfinder: To search for the optimal path. Optimal path are assigned to path and info attribute of the network object.
     network = cutn.Network(*operands_interleave, options=network_opts)
-    network.contract_path(
-        optimize={"samples": n_samples, "threads": ncpu_threads}
-    )  # Calculate optimal path, returns path and info
+    network.contract_path(optimize={"samples": n_samples, "threads": ncpu_threads})
 
+    # Execution: To execute the contraction using the optimal path found previously
     result = network.contract()
 
     cutn.destroy(handle)