diff --git a/examples/dynamo/auto_generate_plugin.py b/examples/dynamo/auto_generate_plugin.py
index 4290745bfd..fc31df8c58 100644
--- a/examples/dynamo/auto_generate_plugin.py
+++ b/examples/dynamo/auto_generate_plugin.py
@@ -108,14 +108,17 @@ def _(x: torch.Tensor, y: torch.Tensor, b: float = 0.2, a: int = 2) -> torch.Ten
 # # -------------------------------------------------------------------
 # # Given that we have defined the custom operator in PyTorch and TensorRT, we can now generate the converter for the operation.
 # # As long as the namespace and names match, the following function will automatically generate the converter for the operation.
+# # If plugins require an output allocator to dynamically allocate output buffers, like data dependent operators, please set requires_output_allocator to True.
 torch_tensorrt.dynamo.conversion.plugins.generate_plugin_converter(
-    "torchtrt_ex::elementwise_scale_mul", supports_dynamic_shapes=True
+    "torchtrt_ex::elementwise_scale_mul",
+    supports_dynamic_shapes=True,
+    requires_output_allocator=False,
 )
 
 
 # # %%
 # # Above two commands can be replaced with the following single one line:
-# torch_tensorrt.dynamo.conversion.plugins.custom_op("torchtrt_ex::elementwise_scale_mul", supports_dynamic_shapes=True)
+# torch_tensorrt.dynamo.conversion.plugins.custom_op("torchtrt_ex::elementwise_scale_mul", supports_dynamic_shapes=True, requires_output_allocator=False)
 
 
 # %%
diff --git a/py/torch_tensorrt/dynamo/conversion/plugins/_custom_op.py b/py/torch_tensorrt/dynamo/conversion/plugins/_custom_op.py
index ef5ed59a56..c936308fd5 100644
--- a/py/torch_tensorrt/dynamo/conversion/plugins/_custom_op.py
+++ b/py/torch_tensorrt/dynamo/conversion/plugins/_custom_op.py
@@ -14,6 +14,7 @@ def custom_op(
     capability_validator: Optional[Callable[[Node, CompilationSettings], bool]] = None,
     priority: ConverterPriority = ConverterPriority.STANDARD,
     supports_dynamic_shapes: bool = False,
+    requires_output_allocator: bool = False,
 ) -> None:
     """
     Generate the Plugin and corresponding Plugin Converter using external kernels and TensorRT Quick Deployable Plugin APIs.
@@ -26,8 +27,13 @@ def custom_op(
             partitioner will make sure this Node is run in PyTorch in the compiled graph.
         priority: Allows developers to override existing converters in the converter registry
         supports_dynamic_shapes: if dynamic shape is supported
+        requires_output_allocator: if the converter creates operators which require an Output Allocator to run (e.g. data dependent operators)
     """
     generate_plugin(op_name)
     generate_plugin_converter(
-        op_name, capability_validator, priority, supports_dynamic_shapes
+        op_name,
+        capability_validator,
+        priority,
+        supports_dynamic_shapes,
+        requires_output_allocator,
     )
diff --git a/py/torch_tensorrt/dynamo/conversion/plugins/_generate_plugin_converter.py b/py/torch_tensorrt/dynamo/conversion/plugins/_generate_plugin_converter.py
index f6343fdb34..8b0e60881a 100644
--- a/py/torch_tensorrt/dynamo/conversion/plugins/_generate_plugin_converter.py
+++ b/py/torch_tensorrt/dynamo/conversion/plugins/_generate_plugin_converter.py
@@ -29,6 +29,7 @@ def _generate_plugin_converter(
     capability_validator: Optional[Callable[[Node, CompilationSettings], bool]] = None,
     priority: ConverterPriority = ConverterPriority.STANDARD,
     supports_dynamic_shapes: bool = False,
+    requires_output_allocator: bool = False,
 ) -> DynamoConverterImplSignature:
     torch_target = getattr(getattr(torch.ops, namespace), op_name)
     overload_str = overload if overload else ""
@@ -87,6 +88,7 @@ def custom_kernel_converter(
         capability_validator=capability_validator,
         priority=priority,
         supports_dynamic_shapes=supports_dynamic_shapes,
+        requires_output_allocator=requires_output_allocator,
     )(custom_kernel_converter)
     assert (
         torch_overload in DYNAMO_CONVERTERS
@@ -99,6 +101,7 @@ def generate_plugin_converter(
     capability_validator: Optional[Callable[[Node, CompilationSettings], bool]] = None,
     priority: ConverterPriority = ConverterPriority.STANDARD,
     supports_dynamic_shapes: bool = False,
+    requires_output_allocator: bool = False,
 ) -> DynamoConverterImplSignature:
     plugin_ns, plugin_name = plugin_id.split("::")
     return _generate_plugin_converter(
@@ -107,4 +110,5 @@ def generate_plugin_converter(
         capability_validator=capability_validator,
         priority=priority,
         supports_dynamic_shapes=supports_dynamic_shapes,
+        requires_output_allocator=requires_output_allocator,
     )