Analyse this Pydantic model and add appropriate statistical distributions to its fields.

**EXISTING MODEL CODE**:
```python
{model_code}
```

**INSTRUCTIONS**:
1. Analyse each field's name, type, and any constraints
2. Add distribution specifications using `Annotated[type, Distribution(...)]` syntax where appropriate
3. Keep semantic/text fields (names, descriptions, emails, bios) as plain types - these will be LLM-generated
4. Use realistic distribution parameters based on field semantics

**AVAILABLE DISTRIBUTIONS**:
- `Normal(mean, std)` - Bell curve: salaries, scores, measurements
- `Uniform(min, max)` - Even spread: ages, random IDs
- `Categorical(weights)` - Weighted categories: departments, statuses, regions
- `LogNormal(mean, sigma)` - Right-skewed: incomes, prices, file sizes
- `Exponential(scale)` - Wait times: response times, time between events
- `Poisson(lam)` - Count data: number of events, errors
- `Beta(alpha, beta)` - Values 0-1: probabilities, rates, percentages
- `Binomial(n, p)` - Success counts: successes in n trials

**DECISION GUIDE**:
- "age" → `Uniform(min=22, max=65)` or similar realistic bounds
- "salary", "price", "income" → `Normal` or `LogNormal` with realistic values
- "count", "number_of" → `Poisson(lam=...)`
- "rate", "percentage", "score" (0-1 range) → `Beta(alpha, beta)`
- "status", "type", "category", "department", "region" → `Categorical(weights={{...}})`
- "name", "description", "email", "title", "bio" → Keep as plain type (NO distribution)

**OUTPUT FORMAT**:
Generate ONLY the class definition with Annotated types added. No imports.

Example input:
```python
class Employee(BaseModel):
    name: str
    age: int
    salary: float
    department: str
```

Example output:
```python
class Employee(BaseModel):
    name: str
    age: Annotated[int, Uniform(min=22, max=65)]
    salary: Annotated[float, Normal(mean=75000, std=20000)]
    department: Annotated[str, Categorical(weights={{"Engineering": 0.4, "Sales": 0.3, "HR": 0.3}})]
```

Generate ONLY the class definition code, no imports or other code.
