A data scientist is attempting to use Spark ML to impute missing values in their PySpark DataFrame 'features_df'. The goal is to replace missing values in all numeric columns with the median of each column. However, the provided code snippet does not achieve this. What is the primary reason the code fails to perform the intended imputation?

my_imputer = imputer(strategy = 'median', inputCols = input_columns, outputCols = output_columns)
imputed_df = my_imputer.transform(features_df)