docs: use flwr 0.19 in example code (#30)

By using this fixed version of flwr, we match the version currently used in the flwr server component and ensure that API changes to flwr don't break the examples.

components/kubeflow-notebooks/requirements.tpl.txt

@@ -1,2 +1,2 @@
 ./tmp/fl_suite-$$container-tag$$-py3-none-any.whl
-flwr==0.18.0
+flwr==0.19.0

docs/quickstart.md

@@ -34,7 +34,7 @@ The `fl_suite.pipelines` package provides functions to describe Federated Learni
 Let's create a simple Federated Learning client that loads the MNIST dataset and trains a simple model. The `pipelines.fl_client` decorator indicates that this is the Federated Learning client code.
 
 ```python
-@pipelines.fl_client(packages=["tensorflow", "flwr"])
+@pipelines.fl_client(packages=["tensorflow", "flwr==0.19.0"])
 def mnist_client():
     import flwr as fl
     import tensorflow as tf

docs/uniwear-example.md

@@ -49,7 +49,7 @@ This package builds a federated learning pipeline. Preparing authentications to
 Let's create a uniwear federated learning client. The client defines the model, loads the uniwear dataset from the repository address, shuffles the rows for regression, prepares test and training set, applies feature scaling, trains a nonlinear regression model, and reports Mean Absolute Percent Error (MAPE) performance per client. 
 
 ```python
-@pipelines.fl_client(packages=["tensorflow", "flwr", "pandas", "scikit-learn"])
+@pipelines.fl_client(packages=["tensorflow", "flwr==0.19.0", "pandas", "scikit-learn"])
 def uniwear_client():
     """Participating tool wear clients will use different experiments."""
     import flwr as fl
@@ -130,7 +130,7 @@ def uniwear_client():
         def evaluate(self, parameters, config):
             model.set_weights(parameters)
             loss, mape = model.evaluate(x_test, y_test, verbose=2)
-            return loss, len(x_val), {"accuracy": mape}
+            return loss, len(x_test), {"accuracy": mape}
 
     fl.client.start_numpy_client("localhost:9080", client=UniWearClient())
 ```