Library 'squared' is deprecated in version 1.4 and will be removed in 1.6.

[DhruvDabas]

Describe the bug
'squared' is deprecated in version 1.4 and will be removed in 1.6.

https://github.com/ageron/handson-ml3/blob/main/10_neural_nets_with_keras.ipynb
Line number 11 in code, inside Regression MLP

To Reproduce

  Changed to import root_mean_squared_error

```python
  from sklearn.datasets import fetch_california_housing
  from sklearn.metrics import mean_squared_error
  from sklearn.model_selection import train_test_split
  from sklearn.neural_network import MLPRegressor
  from sklearn.pipeline import make_pipeline
  from sklearn.preprocessing import StandardScaler
  
  housing = fetch_california_housing()
  X_train_full, X_test, y_train_full, y_test = train_test_split(
      housing.data, housing.target, random_state=42)
  X_train, X_valid, y_train, y_valid = train_test_split(
      X_train_full, y_train_full, random_state=42)
  
  mlp_reg = MLPRegressor(hidden_layer_sizes=[50, 50, 50], random_state=42)
  pipeline = make_pipeline(StandardScaler(), mlp_reg)
  pipeline.fit(X_train, y_train)
  y_pred = pipeline.predict(X_valid)
  rmse = mean_squared_error(y_valid, y_pred, squared=False)

change to the below :

  from sklearn.datasets import fetch_california_housing
  from sklearn.metrics import mean_squared_error, root_mean_squared_error
  from sklearn.model_selection import train_test_split
  from sklearn.neural_network import MLPRegressor
  from sklearn.pipeline import make_pipeline
  from sklearn.preprocessing import StandardScaler
  
  housing = fetch_california_housing()
  X_train_full, X_test, y_train_full, y_test = train_test_split(housing.data, housing.target, random_state=42)
  
  X_train, X_valid, y_train, y_valid = train_test_split(X_train_full, y_train_full, random_state=42)
  
  mlp_reg = MLPRegressor(hidden_layer_sizes=[50, 50, 50], random_state=42)
  pipeline = make_pipeline(StandardScaler(), mlp_reg)
  pipeline.fit(X_train, y_train)
  
  y_pred = pipeline.predict(X_valid)
  rmse = root_mean_squared_error(y_valid, y_pred)
  
  print(rmse)

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The function is depricated kindly update to the above , i have provided the correct code too .