I want to generate a normal (probability) distribution using `numpy.random.normal()`, where **the sum of the probabilities in a row (over all columns) must be 1**.

I used the following code to generate a sample 4 by 3 probability matrix:

```
mu, sigma = 0.5, 0.20  # mean and standard deviation
np.random.seed(40)  
sample_probability = np.random.normal(mu, sigma, size=(4,3))

```

but **the sum of the probabilities in each row becomes larger than 1**, which I don't want to have.

```
 [[0.37849046 0.47477272 0.36307873]
 [0.68574295 0.13111979 0.40659952]
 [0.95849807 0.59776201 0.6420534 ]
 [0.71110689 0.51081462 0.55159068]]

```

i.e. `np.sum(sample_probability[0,:])` yields `1.216341905895543`, **but I want to make it 1**.

Would you please share your insights how I can customize `numpy.random.normal()` so that it limits the distribution of probabilities in a row into 1?

Thanks.

[**UPDATE**] I went for manually normalizing each row, rather introducing the modifications in `numpy.random.normal()`. Thanks to Mikhail and Frank.