fixed lint errors

Signed-off-by: sumana sree <[email protected]>

examples/pydantic_plugin/README.md

@@ -5,11 +5,11 @@
 ```{eval-rst}
 .. tags:: Pydantic, Flytekit
 ```
-[Pydantic](https://pydantic.dev/) is a data validation and settings management library that leverages Python type annotations to enforce type hints at runtime. It provides user-friendly errors when data is invalid, making it easier to ensure data integrity. 
+[Pydantic](https://pydantic.dev/) is a data validation and settings management library that leverages Python type annotations to enforce type hints at runtime. It provides user-friendly errors when data is invalid, making it easier to ensure data integrity.
 
 The Flytekit Pydantic plugin adds type support for Pydantic models, enabling seamless integration of data validation and settings management within Flyte tasks and workflows. This documentation demonstrates how to integrate Pydantic with Flytekit using the Flytekit Pydantic plugin.
 
-## Installation 
+## Installation
 
 To install the Flytekit Pydantic plugin, run the following command:
 

examples/pydantic_plugin/pydantic_plugin/pydantic_integration_example.py

@@ -8,10 +8,12 @@
 # Flyte leverages Pydantic for robust input validation and serialization, ensuring that task inputs are correctly structured.
 
 # %%
-from pydantic.v1 import BaseModel
+from typing import List
+
 from flytekit import task, workflow
 from flytekit.types.file import FlyteFile
-from typing import List
+from pydantic.v1 import BaseModel
+
 
 # %% [markdown]
 # Let's first define a Pydantic model for training configuration.
@@ -21,6 +23,7 @@ class TrainConfig(BaseModel):
     batch_size: int = 32  # Batch size for training
     files: List[FlyteFile]  # List of file inputs for training
 
+
 # %% [markdown]
 # Next, we use the Pydantic model in a Flyte task to train a model.
 # %%
@@ -30,6 +33,7 @@ def train(cfg: TrainConfig):
     for file in cfg.files:
         print(f"Processing file: {file}")
 
+
 # %% [markdown]
 # Now we define a Flyte workflow that utilizes the training task.
 # %%
@@ -38,6 +42,7 @@ def training_workflow(lr: float = 1e-3, batch_size: int = 32, files: List[FlyteF
     cfg = TrainConfig(lr=lr, batch_size=batch_size, files=files)
     train(cfg=cfg)
 
+
 # %% [markdown]
 # Finally, we execute the workflow with sample parameters.
 # %%