input: Write a python function to extract and transform specific columns using pipelines.
  assert extract_transform_columns(X_train, ['capacity', 'date of commission']) is not None
  assert isinstance(extract_transform_columns(X_train, ['capacity', 'date of commission']), pd.DataFrame)
  assert 'capacity' in extract_transform_columns(X_train, ['capacity', 'date of commission']).columns
  assert 'date of commission' in extract_transform_columns(X_train, ['capacity', 'date of commission']).columns
output: def extract_transform_columns(data, columns):
      from sklearn.pipeline import Pipeline
      from sklearn.compose import ColumnTransformer
      from sklearn.preprocessing import StandardScaler, FunctionTransformer
      import pandas as pd
  
      # Define a pipeline that extracts the specified columns and transforms them
      column_transformer = ColumnTransformer(
          transformers=[
              ('selected_columns', 'passthrough', columns)
          ]
      )
  
      pipeline = Pipeline(steps=[
          ('column_transformer', column_transformer)
      ])
      
      transformed_data = pipeline.fit_transform(data)
      
      # Convert the result back to a DataFrame
      transformed_df = pd.DataFrame(transformed_data, columns=columns)
      
      return transformed_df