const artifact: Artifact = {
  id: "artifact_abc123",
  name: "model-weights",
  type: artifactType,
  description: "Trained model weights",
  aliases: ["latest", "production"],
  createdAt: new Date("2024-01-15")
};

const artifactType: ArtifactType = {
  name: "model"
};

const artifactVersion: ArtifactVersion = {
  id: "version_xyz789",
  version: "v3",
  versionIndex: 3,
  aliases: ["latest", "production"],
  createdAt: new Date("2024-01-15"),
  metadata: {
    accuracy: 0.95,
    model_type: "transformer"
  }
};

// Typical ML configuration:
const config: ConfigDict = {
  // Training hyperparameters
  learning_rate: 0.001,
  batch_size: 32,
  epochs: 100,
  optimizer: "adam",
  
  // Model architecture
  model_name: "resnet50",
  num_layers: 50,
  dropout_rate: 0.2,
  hidden_dims: [512, 256, 128],
  
  // Data settings
  dataset: "imagenet",
  augmentation: true,
  train_split: 0.8,
  
  // System settings
  device: "cuda",
  num_workers: 4,
  seed: 42
};

// What you see after logging a W&B Table to config:
const config: ConfigDict = {
  // ... your normal config ...
  
  // This appears automatically when you log wandb.Table() to config:
  "evaluation_results": {
    "_type": "table-file",
    "ncols": 5,
    "nrows": 100,
    "path": "media/table/eval_results_2_abc123.table.json"
  }
};

// What you see after referencing an artifact in config:
const config: ConfigDict = {
  // ... your normal config ...
  
  // This appears when you use an artifact:
  "model_artifact": {
    "_type": "artifactVersion",
    "id": "QXJ0aWZhY3Q6MTIzNDU2",
    "version": "v3",
    "path": "model-weights:v3"
  }
};

// Nested configuration groups
const config: ConfigDict = {
  optimizer: {
    type: "adam",
    betas: [0.9, 0.999],
    weight_decay: 0.0001
  },
  scheduler: {
    type: "cosine",
    warmup_steps: 1000
  }
};

// Environment and metadata
const config: ConfigDict = {
  experiment_name: "baseline_v2",
  git_commit: "abc123def",
  python_version: "3.9.7",
  cuda_version: "11.8"
};

const entity: Entity = {
  id: "entity_abc123",
  name: "my-team",
  isTeam: true
};

const project: Project = {
  name: "my-awesome-project",
  entity: entity,
  createdAt: new Date("2023-01-01"),
  updatedAt: new Date("2024-01-20")
};

const run: Run = {
  id: "run_abc123",
  name: "sunny-dawn-42",
  state: "finished",
  config: {
    learning_rate: 0.001,
    batch_size: 32,
    epochs: 10
  },
  summaryMetrics: {
    loss: 0.023,
    accuracy: 0.95,
    val_accuracy: 0.93
  },
  createdAt: new Date("2024-01-15T10:30:00Z"),
  updatedAt: new Date("2024-01-15T14:45:00Z")
};

// Typical training summary:
const summary: SummaryDict = {
  // Final metrics
  final_loss: 0.0234,
  final_accuracy: 0.9523,
  
  // Best values during training
  best_val_loss: 0.0198,
  best_val_accuracy: 0.9612,
  best_epoch: 87,
  
  // Training statistics
  total_train_time: 3600.5,  // seconds
  total_steps: 50000,
  early_stopped: false,
  
  // Test set results
  test_accuracy: 0.9487,
  test_f1_score: 0.9465
};

// What you see after logging W&B special types to summary:
const summary: SummaryDict = {
  // ... your normal metrics ...
  
  // This appears when you log wandb.Histogram():
  "weight_distribution": {
    "_type": "histogram",
    "bins": [0, 0.1, 0.2, 0.3, 0.4, 0.5],
    "values": [10, 25, 45, 30, 15, 5]
  },
  
  // This appears when you log wandb.Table():
  "predictions_table": {
    "_type": "table-file", 
    "ncols": 4,
    "nrows": 1000,
    "path": "media/table/predictions_3_def456.table.json"
  },
  
  // This appears when you reference an artifact:
  "best_model": {
    "_type": "artifactVersion",
    "id": "QXJ0aWZhY3Q6OTg3NjU0",
    "version": "v12",
    "path": "model-checkpoint:v12"
  }
};

// Grouped metrics by dataset split
const summary: SummaryDict = {
  train: {
    loss: 0.023,
    accuracy: 0.975,
    samples_seen: 50000
  },
  validation: {
    loss: 0.045,
    accuracy: 0.948,
    samples_seen: 10000
  },
  test: {
    loss: 0.041,
    accuracy: 0.951,
    samples_seen: 10000
  }
};

// Multi-class classification results
const summary: SummaryDict = {
  accuracy: 0.92,
  macro_f1: 0.91,
  per_class_precision: [0.95, 0.89, 0.92, 0.90],
  per_class_recall: [0.93, 0.91, 0.90, 0.92],
  confusion_matrix_logged: true  // Actual matrix logged as W&B Table
};

// Model information
const summary: SummaryDict = {
  total_parameters: 125_000_000,
  trainable_parameters: 124_500_000,  
  model_size_mb: 476.8,
  inference_time_ms: 23.4
};

const table: Table = {
  columns: ["epoch", "loss", "accuracy"],
  data: [
    [1, 0.5, 0.75],
    [2, 0.3, 0.85],
    [3, 0.2, 0.90]
  ]
};

const user: User = {
  id: "user_123",
  username: "john_doe",
  name: "John Doe",
  email: "john@example.com"
};