The tool *min* returns the minimum value along a given axis.

```
import numpy
my_array = numpy.array([[2, 5],
[3, 7],
[1, 3],
[4, 0]])
print numpy.min(my_array, axis = 0) #Output : [1 0]
print numpy.min(my_array, axis = 1) #Output : [2 3 1 0]
print numpy.min(my_array, axis = None) #Output : 0
print numpy.min(my_array) #Output : 0
```

By default, the axis value is `None`

. Therefore, it finds the minimum over all the dimensions of the input array.

The tool *max* returns the maximum value along a given axis.

```
import numpy
my_array = numpy.array([[2, 5],
[3, 7],
[1, 3],
[4, 0]])
print numpy.max(my_array, axis = 0) #Output : [4 7]
print numpy.max(my_array, axis = 1) #Output : [5 7 3 4]
print numpy.max(my_array, axis = None) #Output : 7
print numpy.max(my_array) #Output : 7
```

By default, the axis value is `None`

. Therefore, it finds the maximum over all the dimensions of the input array.

**Task**

You are given a 2-D array with dimensions X.

Your task is to perform the *min* function over axis and then find the *max* of that.

**Input Format**

The first line of input contains the space separated values of and .

The next lines contains space separated integers.

**Output Format**

Compute the *min* along axis and then print the *max* of that result.

**Sample Input**

```
4 2
2 5
3 7
1 3
4 0
```

**Sample Output**

```
3
```

**Explanation**

The *min* along axis =

The *max* of =