diff --git a/sdk/python/kfp/components/_python_op.py b/sdk/python/kfp/components/_python_op.py
index 20f63967b3b..e667a2dad0d 100644
--- a/sdk/python/kfp/components/_python_op.py
+++ b/sdk/python/kfp/components/_python_op.py
@@ -377,11 +377,11 @@ def get_deserializer_and_register_definitions(type_name):
     return component_spec
 
 
-def _func_to_component_dict(func, extra_code='', base_image=_default_base_image, modules_to_capture: List[str] = None, use_code_pickling=False):
+def _func_to_component_dict(func, extra_code='', base_image: str = None, modules_to_capture: List[str] = None, use_code_pickling=False):
     return _func_to_component_spec(func, extra_code, base_image, modules_to_capture, use_code_pickling).to_dict()
 
 
-def func_to_component_text(func, extra_code='', base_image=_default_base_image, modules_to_capture: List[str] = None, use_code_pickling=False):
+def func_to_component_text(func, extra_code='', base_image: str = None, modules_to_capture: List[str] = None, use_code_pickling=False):
     '''
     Converts a Python function to a component definition and returns its textual representation
 
@@ -396,8 +396,7 @@ def add_multiply_two_numbers(a: float, b: float) -> NamedTuple('DummyName', [('s
 
     Args:
         func: The python function to convert
-        base_image: Optional. Specify a custom Docker container image to use in the component. For lightweight components, the image needs to have python 3.5+. Default is tensorflow/tensorflow:1.11.0-py3
-                    Note: The image can also be specified by decorating the function with the @python_component decorator. If different base images are explicitly specified in both places, an error is raised.
+        base_image: Optional. Specify a custom Docker container image to use in the component. For lightweight components, the image needs to have python 3.5+. Default is tensorflow/tensorflow:1.13.2-py3
         extra_code: Optional. Extra code to add before the function code. Can be used as workaround to define types used in function signature.
         modules_to_capture: Optional. List of module names that will be captured (instead of just referencing) during the dependency scan. By default the func.__module__ is captured. The actual algorithm: Starting with the initial function, start traversing dependencies. If the dependecy.__module__ is in the modules_to_capture list then it's captured and it's dependencies are traversed. Otherwise the dependency is only referenced instead of capturing and its dependencies are not traversed.
         use_code_pickling: Specifies whether the function code should be captured using pickling as opposed to source code manipulation. Pickling has better support for capturing dependencies, but is sensitive to version mismatch between python in component creation environment and runtime image.
@@ -409,7 +408,7 @@ def add_multiply_two_numbers(a: float, b: float) -> NamedTuple('DummyName', [('s
     return dump_yaml(component_dict)
 
 
-def func_to_component_file(func, output_component_file, base_image=_default_base_image, extra_code='', modules_to_capture: List[str] = None, use_code_pickling=False) -> None:
+def func_to_component_file(func, output_component_file, base_image: str = None, extra_code='', modules_to_capture: List[str] = None, use_code_pickling=False) -> None:
     '''
     Converts a Python function to a component definition and writes it to a file
 
@@ -425,8 +424,7 @@ def add_multiply_two_numbers(a: float, b: float) -> NamedTuple('DummyName', [('s
     Args:
         func: The python function to convert
         output_component_file: Write a component definition to a local file. Can be used for sharing.
-        base_image: Optional. Specify a custom Docker container image to use in the component. For lightweight components, the image needs to have python 3.5+. Default is tensorflow/tensorflow:1.11.0-py3
-                    Note: The image can also be specified by decorating the function with the @python_component decorator. If different base images are explicitly specified in both places, an error is raised.
+        base_image: Optional. Specify a custom Docker container image to use in the component. For lightweight components, the image needs to have python 3.5+. Default is tensorflow/tensorflow:1.13.2-py3
         extra_code: Optional. Extra code to add before the function code. Can be used as workaround to define types used in function signature.
         modules_to_capture: Optional. List of module names that will be captured (instead of just referencing) during the dependency scan. By default the func.__module__ is captured. The actual algorithm: Starting with the initial function, start traversing dependencies. If the dependecy.__module__ is in the modules_to_capture list then it's captured and it's dependencies are traversed. Otherwise the dependency is only referenced instead of capturing and its dependencies are not traversed.
         use_code_pickling: Specifies whether the function code should be captured using pickling as opposed to source code manipulation. Pickling has better support for capturing dependencies, but is sensitive to version mismatch between python in component creation environment and runtime image.
@@ -437,7 +435,7 @@ def add_multiply_two_numbers(a: float, b: float) -> NamedTuple('DummyName', [('s
     Path(output_component_file).write_text(component_yaml)
 
 
-def func_to_container_op(func, output_component_file=None, base_image=_default_base_image, extra_code='', modules_to_capture: List[str] = None, use_code_pickling=False):
+def func_to_container_op(func, output_component_file=None, base_image: str = None, extra_code='', modules_to_capture: List[str] = None, use_code_pickling=False):
     '''
     Converts a Python function to a component and returns a task (ContainerOp) factory
 
@@ -452,8 +450,7 @@ def add_multiply_two_numbers(a: float, b: float) -> NamedTuple('DummyName', [('s
 
     Args:
         func: The python function to convert
-        base_image: Optional. Specify a custom Docker container image to use in the component. For lightweight components, the image needs to have python 3.5+. Default is tensorflow/tensorflow:1.11.0-py3
-                    Note: The image can also be specified by decorating the function with the @python_component decorator. If different base images are explicitly specified in both places, an error is raised.
+        base_image: Optional. Specify a custom Docker container image to use in the component. For lightweight components, the image needs to have python 3.5+. Default is tensorflow/tensorflow:1.13.2-py3
         output_component_file: Optional. Write a component definition to a local file. Can be used for sharing.
         extra_code: Optional. Extra code to add before the function code. Can be used as workaround to define types used in function signature.
         modules_to_capture: Optional. List of module names that will be captured (instead of just referencing) during the dependency scan. By default the func.__module__ is captured. The actual algorithm: Starting with the initial function, start traversing dependencies. If the dependecy.__module__ is in the modules_to_capture list then it's captured and it's dependencies are traversed. Otherwise the dependency is only referenced instead of capturing and its dependencies are not traversed.