diff --git a/test/test_functions.py b/test/test_functions.py
index 0ac7c51..5f830ea 100644
--- a/test/test_functions.py
+++ b/test/test_functions.py
@@ -80,10 +80,30 @@ def test_bernoulli_inplace(self):
 
   def test_flatten(self):
     with self.env:
-      a = torch.randn((2, 3, 4))
+      a = torch.randn((2, 3, 4)).to(device="jax")
       a = a.flatten(0, 1)
       self.assertEqual(tuple(a.shape), (6, 4))
 
+      # New test case for testing tensor flattening on zero dimension
+      a = torch.ones((16, 0)).to(device="jax")
+      a = a.flatten(0, -2)
+      self.assertEqual(a.shape, torch.Size([16, 0]))
+
+      # Flattening tensors with zero dimension containing in flattening dimension
+      a = torch.randn((2, 1, 0, 5)).to(device="jax")
+      a = a.flatten(2, 3)
+      self.assertEqual(a.shape, torch.Size([2, 1, 0]))
+
+      # Flattening tensors with zero dimension containing in non-flattening dimension
+      a = torch.randn((2, 0, 1, 5)).to(device="jax")
+      a = a.flatten(2, 3)
+      self.assertEqual(a.shape, torch.Size([2, 0, 5]))
+
+      # Flattening tensors with zero dimension containing in flattening dimension and non-flattening dimension
+      a = torch.randn((2, 0, 0, 5)).to(device="jax")
+      a = a.flatten(2, 3)
+      self.assertEqual(a.shape, torch.Size([2, 0, 0]))
+
   def test_rnn(self):
     model = SeqModel()
     x = torch.randn((2, 100, 20))
diff --git a/torchax/tensor.py b/torchax/tensor.py
index 441ac82..973db82 100644
--- a/torchax/tensor.py
+++ b/torchax/tensor.py
@@ -15,6 +15,7 @@
 import contextlib
 import itertools
 import logging
+import math
 import sys
 import threading
 from collections.abc import Callable
@@ -96,7 +97,10 @@ def ndim(self):
   def flatten(self, start_dim=0, end_dim=-1):
     if end_dim == -1:
       end_dim = self.ndim
-    new_shape = self._elem.shape[:start_dim] + (-1,) + self._elem.shape[end_dim + 1 :]
+    flattened_size = math.prod(self._elem.shape[start_dim : end_dim + 1])
+    new_shape = (
+      self._elem.shape[:start_dim] + (flattened_size,) + self._elem.shape[end_dim + 1 :]
+    )
     new_elem = jnp.reshape(self._elem, new_shape)
     return Tensor(new_elem, self._env)
     # return torch.reshape(self, new_shape)