diff --git a/ml4gw/distributions.py b/ml4gw/distributions.py
index 4609ddad..a4588ae9 100644
--- a/ml4gw/distributions.py
+++ b/ml4gw/distributions.py
@@ -5,6 +5,7 @@
 from the corresponding distribution.
 """
 
+import math
 from typing import Optional
 
 import torch
@@ -21,14 +22,15 @@ class Cosine(dist.Distribution):
 
     def __init__(
         self,
-        low: float = torch.as_tensor(-torch.pi / 2),
-        high: float = torch.as_tensor(torch.pi / 2),
+        low: float = -math.pi / 2,
+        high: float = math.pi / 2,
         validate_args=None,
     ):
         batch_shape = torch.Size()
         super().__init__(batch_shape, validate_args=validate_args)
-        self.low = low
-        self.norm = 1 / (torch.sin(high) - torch.sin(low))
+        self.low = torch.as_tensor(low)
+        self.high = torch.as_tensor(high)
+        self.norm = 1 / (torch.sin(self.high) - torch.sin(self.low))
 
     def rsample(self, sample_shape: torch.Size = torch.Size()) -> torch.Tensor:
         u = torch.rand(sample_shape, device=self.low.device)
@@ -48,13 +50,16 @@ class Sine(dist.TransformedDistribution):
 
     def __init__(
         self,
-        low: float = torch.as_tensor(0),
-        high: float = torch.as_tensor(torch.pi),
+        low: float = 0.0,
+        high: float = math.pi,
         validate_args=None,
     ):
+        low = torch.as_tensor(low)
+        high = torch.as_tensor(high)
         base_dist = Cosine(
             low - torch.pi / 2, high - torch.pi / 2, validate_args
         )
+
         super().__init__(
             base_dist,
             [
@@ -153,12 +158,12 @@ class DeltaFunction(dist.Distribution):
 
     def __init__(
         self,
-        peak: float = torch.as_tensor(0.0),
+        peak: float = 0.0,
         validate_args=None,
     ):
         batch_shape = torch.Size()
         super().__init__(batch_shape, validate_args=validate_args)
-        self.peak = peak
+        self.peak = torch.as_tensor(peak)
 
     def rsample(self, sample_shape: torch.Size = torch.Size()) -> torch.Tensor:
         return self.peak * torch.ones(
diff --git a/tests/test_distributions.py b/tests/test_distributions.py
index 6f84e6e7..8ebed558 100644
--- a/tests/test_distributions.py
+++ b/tests/test_distributions.py
@@ -1,8 +1,8 @@
+import math
+
 import numpy as np
 import pytest
-import torch
 from scipy import optimize
-from torch import pi
 
 from ml4gw import distributions
 
@@ -11,10 +11,10 @@
 
 
 def test_log_uniform():
-    sampler = distributions.LogUniform(torch.e, torch.e**2)
+    sampler = distributions.LogUniform(math.e, math.e**2)
     samples = sampler.sample((10,))
     assert len(samples) == 10
-    assert ((torch.e <= samples) & (torch.e**2 <= 100)).all()
+    assert ((math.e <= samples) & (math.e**2 <= 100)).all()
 
     # check that the mean is roughly correct
     # (within three standard deviations)
@@ -32,9 +32,9 @@ def test_cosine():
     sampler = distributions.Cosine()
     samples = sampler.sample((10,))
     assert len(samples) == 10
-    assert ((-pi / 2 <= samples) & (samples <= pi / 2)).all()
+    assert ((-math.pi / 2 <= samples) & (samples <= math.pi / 2)).all()
 
-    sampler = distributions.Cosine(torch.as_tensor(-3), torch.as_tensor(5))
+    sampler = distributions.Cosine(-3, 5)
     samples = sampler.sample((100,))
     assert len(samples) == 100
     assert ((-3 <= samples) & (samples <= 5)).all()
@@ -82,6 +82,6 @@ def foo(x, a, b):
 
 
 def test_delta_function():
-    sampler = distributions.DeltaFunction(peak=torch.as_tensor(20))
+    sampler = distributions.DeltaFunction(peak=20)
     samples = sampler.sample((10,))
     assert (samples == 20).all()