Implement **whitespace tokenization** for a simple NLP preprocessing step, where you split text into tokens by runs of whitespace and discard empty tokens. Return the tokens in order so they can be fed into later steps like vocabulary building.

## Requirements

Implement the function
```python
def whitespace_tokenize(text):
    \"""
    Splits a string into tokens using whitespace.

    Args:
        text (str): Input text.

    Returns:
        np.ndarray: Tokens in the original order.
    \"""
    # Your solution below
```

**Rules:**
- Tokenize by splitting on **one or more** whitespace characters (spaces, tabs, newlines).
- Do not include any empty tokens in the output.
- Keep punctuation attached to words (e.g., `"hello,"` stays as `"hello,"`).
- Do not lowercase or normalize; preserve the original characters.
- Use only **NumPy** and Python built-in libraries.
- Return a **NumPy array** of strings.

## Example

```python
text = "Hello,\tworld!\nThis  is   NLP."
whitespace_tokenize(text)
```

Output:
```python
array(["Hello,", "world!", "This", "is", "NLP."], dtype='<U6')
```

Inverse Transform Sampling

Instrumental Variable Estimator

  Leading and trailing   spaces		and
newlines
  

Unicode NBSP and	emojis 😀😃 plus
multiple spaces

Whitespace tokenization

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1438. Whitespace tokenization

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