For sharded weights let's not delete explicitly

Author: amitsrivastava78

keras_hub/src/utils/preset_utils.py

@@ -502,10 +502,15 @@ def jax_memory_cleanup(layer):
     # For jax, delete all previous allocated memory to avoid temporarily
     # duplicating variable allocations. torch and tensorflow have stateful
     # variable types and do not need this fix.
+    # Skip deletion for sharded arrays to avoid breaking references in distributed setups.
     if keras.config.backend() == "jax":
         for weight in layer.weights:
             if getattr(weight, "_value", None) is not None:
-                weight._value.delete()
+                # Do not delete sharded arrays, as they may be referenced in JAX's
+                # distributed computation graph and deletion can cause errors.
+                if not (hasattr(weight._value, 'sharding')
+                                    and weight._value.sharding is not None):
+                    weight._value.delete()
 
 
 def set_dtype_in_config(config, dtype=None):