diff --git a/jax/experimental/array.py b/jax/experimental/array.py
index 59e5aef22..a165d8695 100644
--- a/jax/experimental/array.py
+++ b/jax/experimental/array.py
@@ -118,7 +118,10 @@ class Array:
     self._committed = committed
     self._npy_value = None
 
-    if config.jax_enable_checks:
+    # TODO(yashkatariya): Add a check here which checks if the expected shard
+    # shape matches the shape of _arrays. A similar check exists for GDA.
+
+    if not _skip_checks or config.jax_enable_checks:
       assert all(db.dtype == self.dtype for db in self._arrays), (
           "Input arrays to `Array` must have matching dtypes, "
           f"got: {[db.dtype for db in self._arrays]}, aval type: {self.dtype}")
@@ -235,6 +238,9 @@ class Array:
     else:
       raise TypeError(self.dtype)
 
+  def is_fully_replicated(self) -> bool:
+    return self.shape == self._arrays[0].shape
+
   def __repr__(self):
     prefix = '{}('.format(self.__class__.__name__.lstrip('_'))
     if self.aval is not None and self.aval.weak_type:
@@ -242,7 +248,7 @@ class Array:
     else:
       dtype_str = f'dtype={self.dtype.name})'
 
-    if self.is_fully_addressable():
+    if self.is_fully_addressable() or self.is_fully_replicated():
       line_width = np.get_printoptions()["linewidth"]
       s = np.array2string(self._value, prefix=prefix, suffix=',',
                           separator=', ', max_line_width=line_width)
@@ -340,15 +346,9 @@ class Array:
     self._check_if_deleted()
 
     if self._npy_value is None:
-      if isinstance(self.sharding, XLACompatibleSharding):
-        try:
-          op_sharding = self.sharding._to_xla_op_sharding(self.ndim)
-          assert op_sharding is not None
-          if pxla.is_op_sharding_replicated(op_sharding):
-            self._npy_value = self._arrays[0].to_py()  # type: ignore
-            return cast(np.ndarray, self._npy_value)
-        except NotImplementedError:
-          pass
+      if self.is_fully_replicated():
+        self._npy_value = np.asarray(self._arrays[0])  # type: ignore
+        return cast(np.ndarray, self._npy_value)
 
       if not self.is_fully_addressable():
         raise RuntimeError("Fetching value for `jax.Array` that spans "