diff --git a/experimental/torch_xla2/examples/lightning_training.py b/experimental/torch_xla2/examples/lightning_training.py
new file mode 100644
index 00000000000..b09f00d9473
--- /dev/null
+++ b/experimental/torch_xla2/examples/lightning_training.py
@@ -0,0 +1,77 @@
+import os, torch, torch.nn as nn, torch.utils.data as data, torchvision as tv
+import lightning as L
+
+encoder = nn.Sequential(nn.Linear(28 * 28, 128), nn.ReLU(), nn.Linear(128, 3))
+decoder = nn.Sequential(nn.Linear(3, 128), nn.ReLU(), nn.Linear(128, 28 * 28))
+
+class LitAutoEncoder(L.LightningModule):
+    def __init__(self, encoder, decoder):
+        super().__init__()
+        self.encoder, self.decoder = encoder, decoder
+
+    def training_step(self, batch, batch_idx):
+        x, y = batch
+        x = x.view(x.size(0), -1)
+        z = self.encoder(x)
+        x_hat = self.decoder(z)
+        loss = nn.functional.mse_loss(x_hat, x)
+        self.log("train_loss", loss)
+        return loss
+
+    def configure_optimizers(self):
+        return torch.optim.Adam(self.parameters(), lr=1e-3)
+
+dataset = tv.datasets.MNIST(".", download=True, transform=tv.transforms.ToTensor())
+
+# Lightning will automatically use all available GPUs!
+trainer = L.Trainer()
+# trainer.fit(LitAutoEncoder(encoder, decoder), data.DataLoader(dataset, batch_size=64))
+
+# ==== above is the lightning example from 
+# https://lightning.ai/pytorch-lightning
+
+import torch_xla2
+from torch_xla2.interop import jax_view, torch_view
+import jax
+import optax
+
+class JaxTrainer:
+
+    def __init__(self):
+        pass
+
+    def torch_opt_to_jax_opt(self, torch_opt):
+        # TODO: Can convert optimizer instead of using a jax one
+        return optax.adam(0.001)
+
+    def fit(self, lightning_mod, data_loader):
+
+        xla_env = torch_xla2.default_env()
+
+        def lightning_mod_loss(
+            weights: jax.Array, data: jax.Array, batch_id):
+            """returns loss"""
+            weights, data = torch_view((weights, data))
+            lightning_mod.load_state_dict(weights, assign=True)
+            with xla_env:
+                loss = lightning_mod.training_step(data, batch_id)
+            return jax_view(loss)
+
+        jax_weights = jax_view(xla_env.to_xla(lightning_mod.state_dict()))
+        jax_optimizer = self.torch_opt_to_jax_opt(
+            lightning_mod.configure_optimizers())
+        opt_state = jax_optimizer.init(jax_weights)
+        grad_fn = jax.jit(jax.value_and_grad(lightning_mod_loss))
+
+        for bid in range(3):
+            for item in data_loader:
+                xla_data = jax_view(xla_env.to_xla(item))
+                loss, grads = grad_fn(jax_weights, xla_data, bid)
+                updates, opt_state = jax_optimizer.update(grads, opt_state)
+                jax_weights = optax.apply_updates(jax_weights, updates)
+                print('current_loss', loss)
+
+
+print('-----------------')
+trainer_jax = JaxTrainer()
+trainer_jax.fit(LitAutoEncoder(encoder, decoder), data.DataLoader(dataset, batch_size=64))
diff --git a/experimental/torch_xla2/torch_xla2/interop.py b/experimental/torch_xla2/torch_xla2/interop.py
index fbcd47922e1..d1a96179e82 100644
--- a/experimental/torch_xla2/torch_xla2/interop.py
+++ b/experimental/torch_xla2/torch_xla2/interop.py
@@ -10,8 +10,7 @@
 
 
 
-
-def torch_view(t: JaxValue) -> TorchValue:
+def _torch_view(t: JaxValue) -> TorchValue:
     # t is an object from jax land
     # view it as-if it's a torch land object
     if isinstance(t, jax.Array):
@@ -24,8 +23,10 @@ def torch_view(t: JaxValue) -> TorchValue:
     # regular types are not changed
     return t
 
+torch_view = functools.partial(pytree.tree_map, _torch_view)
+
 
-def jax_view(t: TorchValue) -> JaxValue:
+def _jax_view(t: TorchValue) -> JaxValue:
     # t is an object from torch land
     # view it as-if it's a jax land object
     if isinstance(t, torch.Tensor):
@@ -40,17 +41,19 @@ def jax_view(t: TorchValue) -> JaxValue:
     # regular types are not changed
     return t
 
+jax_view = functools.partial(pytree.tree_map, _jax_view)
+
 
 def call_jax(jax_func: JaxCallable, 
              *args: TorchValue, 
              **kwargs: TorchValue) -> TorchValue:
-    args, kwargs = pytree.tree_map(jax_view, (args, kwargs))
+    args, kwargs = jax_view((args, kwargs))
     res: JaxValue = jax_func(*args, **kwargs)
     return torch_view(res)
 
 
 def call_torch(torch_func: TorchCallable, *args: JaxValue, **kwargs: JaxValue) -> JaxValue:
-    args, kwargs = pytree.tree_map(torch_view, (args, kwargs))
+    args, kwargs = torch_view((args, kwargs))
     with torch_xla2.default_env():
         res: TorchValue = torch_func(*args, **kwargs)
     return jax_view(res)
@@ -63,3 +66,4 @@ def jax_jit(torch_function, kwargs_for_jax_jit=None):
     jax_func = jax_view(torch_function)
     jitted = jax.jit(jax_func, **kwargs_for_jax_jit)
     return torch_view(jitted)
+
diff --git a/experimental/torch_xla2/torch_xla2/ops/jaten.py b/experimental/torch_xla2/torch_xla2/ops/jaten.py
index f6adc702a14..3c92e5e290d 100644
--- a/experimental/torch_xla2/torch_xla2/ops/jaten.py
+++ b/experimental/torch_xla2/torch_xla2/ops/jaten.py
@@ -1827,6 +1827,7 @@ def _aten_tensor_split(ary, indices_or_sections, axis=0):
 
 
 @op(torch.ops.aten.randn, needs_env=True)
+@op_base.convert_dtype()
 def _randn(
   *size,
   generator=None,
@@ -1844,12 +1845,12 @@ def _randn(
   key = env.get_and_rotate_prng_key()
   res = jax.random.normal(key, shape)
   if dtype is not None:
-    dtype = tensor.t2j_dtype(dtype)
     res = res.astype(dtype)
   return res
 
 
 @op(torch.ops.aten.rand, needs_env=True)
+@op_base.convert_dtype()
 def _rand(
   *size,
   generator=None,
@@ -1867,7 +1868,6 @@ def _rand(
   key = env.get_and_rotate_prng_key()
   res = jax.random.uniform(key, shape)
   if dtype is not None:
-    dtype = tensor.t2j_dtype(dtype)
     res = res.astype(dtype)
   return res
 
diff --git a/experimental/torch_xla2/torch_xla2/ops/jtorch.py b/experimental/torch_xla2/torch_xla2/ops/jtorch.py
index ddc04fa4b1b..6d7003b936e 100644
--- a/experimental/torch_xla2/torch_xla2/ops/jtorch.py
+++ b/experimental/torch_xla2/torch_xla2/ops/jtorch.py
@@ -1,49 +1,21 @@
 """Tensor constructor overrides"""
 import functools
-from typing import Callable, Optional, ParamSpec, Sequence
+from typing import Optional, Sequence
 
 import jax
 import torch
 import jax.numpy as jnp
 from torch_xla2 import tensor
 from torch_xla2.ops.ops_registry import register_torch_function_op
+from torch_xla2.ops import op_base
+
 
 def register_function(torch_func, **kwargs):
   return functools.partial(register_torch_function_op, torch_func, **kwargs)
 
 
-P = ParamSpec('P')
-
-
-def convert_dtype(use_default_dtype: bool = True):
-  """Converts `dtype` kwarg of function from torch to JAX.
-
-  Args:
-    use_default_dtype: Whether to use torch default dtype if none is provided.
-
-  Returns:
-    A decorator that wraps a JAX implementation of a torch function.
-  """
-
-  def decorator(func: Callable[P, torch.Tensor]):
-
-    @functools.wraps(func)
-    def wrapper(*args: P.args,
-                dtype: Optional[torch.dtype] = None,
-                **kwargs: P.kwargs):
-      if not dtype and use_default_dtype:
-        dtype = torch.get_default_dtype()
-      jax_dtype = tensor.t2j_dtype(dtype)
-
-      return func(*args, dtype=jax_dtype, **kwargs)
-
-    return wrapper
-
-  return decorator
-
-
 @register_function(torch.tensor)
-@convert_dtype(use_default_dtype=False)  # Attempt to infer type from elements
+@op_base.convert_dtype(use_default_dtype=False)  # Attempt to infer type from elements
 def _tensor(data, *, dtype=None, **kwargs):
   python_types_to_torch_types = {
       bool: jnp.bool,
@@ -61,25 +33,25 @@ def _tensor(data, *, dtype=None, **kwargs):
 
 
 @register_function(torch.ones)
-@convert_dtype()
+@op_base.convert_dtype()
 def _ones(*size: int, dtype=None, **kwargs):
   return jnp.ones(size, dtype)
 
 
 @register_function(torch.zeros)
-@convert_dtype()
+@op_base.convert_dtype()
 def _zeros(*size: int, dtype=None, **kwargs):
   return jnp.zeros(size, dtype)
 
 
 @register_function(torch.eye)
-@convert_dtype()
+@op_base.convert_dtype()
 def _eye(n: int, m: Optional[int] = None, *, dtype=None, **kwargs):
   return jnp.eye(n, m, dtype=dtype)
 
 
 @register_function(torch.full)
-@convert_dtype()
+@op_base.convert_dtype()
 def _full(size: Sequence[int], fill_value, *, dtype=None, **kwargs):
   # TODO: handle torch.Size
   return jnp.full(size, fill_value, dtype=dtype)
diff --git a/experimental/torch_xla2/torch_xla2/ops/op_base.py b/experimental/torch_xla2/torch_xla2/ops/op_base.py
index 983d20fb660..8076a11fb09 100644
--- a/experimental/torch_xla2/torch_xla2/ops/op_base.py
+++ b/experimental/torch_xla2/torch_xla2/ops/op_base.py
@@ -1,5 +1,9 @@
+import functools
 import torch
-from torch_xla2 import interop
+from torch_xla2 import interop, tensor
+from torch_xla2 import types
+
+from typing import Callable, Optional, ParamSpec, Sequence
 
 
 class BinaryOpWithPromotion:
@@ -52,4 +56,29 @@ def __call__(self, *args, **kwargs):
 
 
 
+P = ParamSpec('P')
+def convert_dtype(use_default_dtype: bool = True):
+  """Converts `dtype` kwarg of function from torch to JAX.
+
+  Args:
+    use_default_dtype: Whether to use torch default dtype if none is provided.
+
+  Returns:
+    A decorator that wraps a JAX implementation of a torch function.
+  """
+
+  def decorator(func: types.TorchCallable):
+
+    @functools.wraps(func)
+    def wrapper(*args: P.args,
+                dtype: Optional[torch.dtype] = None,
+                **kwargs: P.kwargs):
+      if not dtype and use_default_dtype:
+        dtype = torch.get_default_dtype()
+      jax_dtype = tensor.t2j_dtype(dtype)
+
+      return func(*args, dtype=jax_dtype, **kwargs)
+
+    return wrapper
 
+  return decorator