Impute missing values without leakage

Given a DataFrame df with columns: user_id, event_date (datetime), country (categorical), device_type (categorical), age (numeric), income (numeric), last_purchase_days_ago (numeric), session_length (numeric), is_active_30d (binary label). Implement code to impute missing values for model training with strict no‑leakage. Requirements: 1) Split into train/validation indices; all statistics/models for imputation must be fit on train only and then applied to validation. 2) Numeric: age → median within country (train‑only medians); income → train a ridge regression imputer on train rows using predictors [age_imputed, country, device_type, last_purchase_days_ago, session_length] (one‑hot encoded), then predict income for both train/validation; do not use the label. 3) Time‑ordered within user: for last_purchase_days_ago and session_length, sort by event_date per user_id and forward‑fill gaps up to 14 days; if the gap between consecutive event_date exceeds 14 days, do not propagate; after sequence fills, fill remaining NaNs with the global train median for that feature. 4) Categoricals: device_type and country → per‑user mode; break ties with the global train mode. 5) Deliver: functions fit_imputers(df, train_idx) and transform_impute(df, imputers, idx) where imputers holds all train‑fit objects/statistics; include assertions that no value derived from validation data was used to compute train statistics.