diff --git a/pina/pinn.py b/pina/pinn.py
index 860c464..1ca2636 100644
--- a/pina/pinn.py
+++ b/pina/pinn.py
@@ -3,6 +3,7 @@ import torch
 
 from .problem import AbstractProblem
 from .label_tensor import LabelTensor
+from .utils import merge_tensors
 
 torch.pi = torch.acos(torch.zeros(1)).item() * 2 # which is 3.1415927410125732
 
@@ -126,27 +127,6 @@ class PINN(object):
         >>> pinn.span_pts(n=10, mode='grid', location=['bound1'])
         >>> pinn.span_pts(n=10, mode='grid', variables=['x'])
         """
-
-        def merge_tensors(tensors):  # name to be changed
-            if len(tensors) == 2:
-                tensor1 = tensors[0]
-                tensor2 = tensors[1]
-                n1 = tensor1.shape[0]
-                n2 = tensor2.shape[0]
-
-                tensor1 = LabelTensor(
-                    tensor1.repeat(n2, 1),
-                    labels=tensor1.labels)
-                tensor2 = LabelTensor(
-                    tensor2.repeat_interleave(n1, dim=0),
-                    labels=tensor2.labels)
-                return tensor1.append(tensor2)
-            elif len(tensors) == 1:
-                return tensors[0]
-            else:
-                recursive_result = merge_tensors(tensors[1:])
-                return merge_tensors([tensors[0], recursive_result])
-
         if isinstance(args[0], int) and isinstance(args[1], str):
             argument = {}
             argument['n'] = int(args[0])
@@ -171,19 +151,20 @@ class PINN(object):
         for location in locations:
             condition = self.problem.conditions[location]
 
-            pts = merge_tensors([
-                condition.location.sample(
-                    argument['n'],
-                    argument['mode'],
-                    variables=argument['variables'])
-                for argument in arguments])
+            samples = tuple(condition.location.sample(
+                            argument['n'],
+                            argument['mode'],
+                            variables=argument['variables'])
+                        for argument in arguments)
+            pts = merge_tensors(samples)
 
-            self.input_pts[location] = pts  #.double()  # TODO
-            self.input_pts[location] = (
-                self.input_pts[location].to(dtype=self.dtype,
-                                            device=self.device))
-            self.input_pts[location].requires_grad_(True)
-            self.input_pts[location].retain_grad()
+            # TODO
+            # pts = pts.double()
+            pts = pts.to(dtype=self.dtype, device=self.device)
+            pts.requires_grad_(True)
+            pts.retain_grad()
+
+            self.input_pts[location] = pts
 
     def train(self, stop=100, frequency_print=2, save_loss=1, trial=None):
 
@@ -193,17 +174,14 @@ class PINN(object):
         for condition_name in self.problem.conditions:
             condition = self.problem.conditions[condition_name]
 
-            if hasattr(condition, 'function'):
-                if isinstance(condition.function, list):
-                    for function in condition.function:
-                        header.append(f'{condition_name}{function.__name__}')
-
-                    continue
-
-            header.append(f'{condition_name}')
+            if (hasattr(condition, 'function') and
+                    isinstance(condition.function, list)):
+                for function in condition.function:
+                    header.append(f'{condition_name}{function.__name__}')
+            else:
+                header.append(f'{condition_name}')
 
         while True:
-
             losses = []
 
             for condition_name in self.problem.conditions:
diff --git a/pina/utils.py b/pina/utils.py
index 769ee97..ed18af0 100644
--- a/pina/utils.py
+++ b/pina/utils.py
@@ -1,9 +1,11 @@
 """Utils module"""
+from functools import reduce
+
 
 def number_parameters(model, aggregate=True, only_trainable=True): #TODO: check
     """
     Return the number of parameters of a given `model`.
-    
+
     :param torch.nn.Module model: the torch module to inspect.
     :param bool aggregate: if True the return values is an integer corresponding
         to the total amount of parameters of whole model. If False, it returns a
@@ -25,3 +27,19 @@ def number_parameters(model, aggregate=True, only_trainable=True): #TODO: check
         tmp = sum(tmp.values())
 
     return tmp
+
+
+def merge_tensors(tensors):  # name to be changed
+    if tensors:
+        return reduce(merge_two_tensors, tensors[1:], tensors[0])
+    raise ValueError("Expected at least one tensor")
+
+
+def merge_two_tensors(tensor1, tensor2):
+    n1 = tensor1.shape[0]
+    n2 = tensor2.shape[0]
+
+    tensor1 = LabelTensor(tensor1.repeat(n2, 1), labels=tensor1.labels)
+    tensor2 = LabelTensor(tensor2.repeat_interleave(n1, dim=0),
+                            labels=tensor2.labels)
+    return tensor1.append(tensor2)