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I am creating a multi-dimension *numpy* matrix like this:

```
a = np.array([255, 0])
mins_and_maxes = np.tile(a, [9, 2, 43])
```

I’m expecting `mins_and_maxes`

to be a 4-D array with a shape of *(9, 2, 43, 2)*. However, `mins_and_maxes`

has a shape of *(9, 2, 86)*. The *[255, 0]* arrays are sort of being ‘dissolved’. (I can’t think of a better word. "Exploded"?)

How do I get a matrix of size *(9, 2, 43)* where every element is a copy of the array of length *2*, *[255, 0]*?

### >Solution :

You can try:

```
a = np.array([255, 0])
mins_and_maxes = np.tile(a, [9, 2, 43, 1])
mins_and_maxes.shape
#(9, 2, 43, 2)
```

mins_and_maxes

```
#array([[[[255, 0],
[255, 0],
[255, 0],
...,
[255, 0],
[255, 0],
[255, 0]],
[[[255, 0],
[255, 0],
[255, 0],
...,
[255, 0],
[255, 0],
[255, 0]],
[[255, 0],
[255, 0],
[255, 0],
...,
[255, 0],
[255, 0],
[255, 0]]],
[[[255, 0],
[255, 0],
[255, 0],
...,
[255, 0],
[255, 0],
[255, 0]],
[[255, 0],
[255, 0],
[255, 0],
...,
[255, 0],
[255, 0],
[255, 0]]]])
```