fix lint, formatting

Signed-off-by: Niels Bantilan <[email protected]>

examples/basics/basics/task.py

@@ -34,17 +34,14 @@
 from sklearn.linear_model import LogisticRegression
 from sklearn.model_selection import train_test_split
 
-
 # %% [markdown]
 # The use of the :py:func:`flytekit.task` decorator is mandatory for a ``PythonFunctionTask``.
 # A task is essentially a regular Python function, with the exception that all inputs and outputs must be clearly annotated with their types.
 # These types are standard Python types, which will be further explained in the :ref:`type-system section <flytekit_to_flyte_type_mapping>`.
 
 # %%
 @task
-def train_model(
-    hyperparameters: dict, test_size: float, random_state: int
-) -> LogisticRegression:
+def train_model(hyperparameters: dict, test_size: float, random_state: int) -> LogisticRegression:
     """
     Parameters:
         hyperparameters (dict): A dictionary containing the hyperparameters for the model.
@@ -58,9 +55,7 @@ def train_model(
     iris = load_iris()
 
     # Splitting the data into train and test sets
-    X_train, _, y_train, _ = train_test_split(
-        iris.data, iris.target, test_size=test_size, random_state=random_state
-    )
+    X_train, _, y_train, _ = train_test_split(iris.data, iris.target, test_size=test_size, random_state=random_state)
 
     # Creating and training the logistic regression model with the given hyperparameters
     clf = LogisticRegression(**hyperparameters)
@@ -97,29 +92,28 @@ def train_model_wf(
     """
     This workflow invokes the train_model task with the given hyperparameters, test size and random state.
     """
-    return train_model(
-        hyperparameters=hyperparameters, test_size=test_size, random_state=random_state
-    )
+    return train_model(hyperparameters=hyperparameters, test_size=test_size, random_state=random_state)
 
 
-# %%
+# %% [markdown]
 # ```{note}
 # When invoking the `train_model` task, you need to use keyword arguments to specify the values for the corresponding parameters.
 # ````
 #
 # ## Use `partial` to provide default arguments to tasks
 #
 # You can use the {py:func}`functools.partial` function to assign default or constant values to the parameters of your tasks.
+
+# %%
 import functools
 
 
 @workflow
-def train_model_wf_with_partial(
-    test_size: float = 0.2, random_state: int = 42
-) -> LogisticRegression:
+def train_model_wf_with_partial(test_size: float = 0.2, random_state: int = 42) -> LogisticRegression:
     partial_task = functools.partial(train_model, hyperparameters={"C": 0.1})
     return partial_task(test_size=test_size, random_state=random_state)
 
+
 # %% [markdown]
 # In this toy example, we're calling the `square` task twice and returning the result.
 

examples/control_flow/control_flow/map_task.py

@@ -55,7 +55,7 @@ def coalesce(b: List[str]) -> str:
 # To repeat the execution of the `a_mappable_task` across a collection of inputs, use the {py:func}`~flytekit:flytekit.map_task` function from flytekit.
 # In this example, the input `a` is of type `List[int]`.
 # The `a_mappable_task` is executed for each element in the list.
-# 
+#
 # You can utilize the `with_overrides` function to set resources specifically for individual map tasks.
 # This allows you to customize resource allocations such as memory usage.
 
@@ -129,10 +129,9 @@ def multi_input_task(quantity: int, price: float, shipping: float) -> float:
 # %%
 import functools
 
+
 @workflow
-def multiple_workflow(
-    list_q: List[int] = [1, 2, 3, 4, 5], p: float = 6.0, s: float = 7.0
-) -> List[float]:
+def multiple_workflow(list_q: List[int] = [1, 2, 3, 4, 5], p: float = 6.0, s: float = 7.0) -> List[float]:
     partial_task = functools.partial(multi_input_task, price=p, shipping=s)
     return map_task(partial_task)(quantity=list_q)
 
@@ -147,9 +146,7 @@ def get_price() -> float:
 
 
 @workflow
-def multiple_workflow_with_task_output(
-    list_q: List[int] = [1, 2, 3, 4, 5], s: float = 6.0
-) -> List[float]:
+def multiple_workflow_with_task_output(list_q: List[int] = [1, 2, 3, 4, 5], s: float = 6.0) -> List[float]:
     p = get_price()
     partial_task = functools.partial(multi_input_task, price=p, shipping=s)
     return map_task(partial_task)(quantity=list_q)
@@ -160,10 +157,13 @@ def multiple_workflow_with_task_output(
 
 # %%
 @workflow
-def multiple_workflow_with_lists(list_q: List[int] = [1, 2, 3, 4, 5], list_p: List[float] = [6.0, 9.0, 8.7, 6.5, 1.2], s: float = 6.0) -> List[float]:
+def multiple_workflow_with_lists(
+    list_q: List[int] = [1, 2, 3, 4, 5], list_p: List[float] = [6.0, 9.0, 8.7, 6.5, 1.2], s: float = 6.0
+) -> List[float]:
     partial_task = functools.partial(multi_input_task, shipping=s)
     return map_task(partial_task)(quantity=list_q, price=list_p)
 
+
 # %% [markdown]
 # ```{note}
 # It is important to note that you cannot provide a list as an input to a partial task.