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# numpy.core.umath.minimum.reduce

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```reduce(a, axis=0, dtype=None, out=None, keepdims=False)

Reduces `a`'s dimension by one, by applying ufunc along one axis.

Let :math:`a.shape = (N_0, ..., N_i, ..., N_{M-1})`.  Then
:math:`ufunc.reduce(a, axis=i)[k_0, ..,k_{i-1}, k_{i+1}, .., k_{M-1}]` =
the result of iterating `j` over :math:`range(N_i)`, cumulatively applying
ufunc to each :math:`a[k_0, ..,k_{i-1}, j, k_{i+1}, .., k_{M-1}]`.
For a one-dimensional array, reduce produces results equivalent to:
::(more...)
```

```def _amin(a, axis=None, out=None, keepdims=False):
return um.minimum.reduce(a, axis=axis,
out=out, keepdims=keepdims)

def _sum(a, axis=None, dtype=None, out=None, keepdims=False):
```

```def _amin(a, axis=None, out=None, keepdims=False):
return um.minimum.reduce(a, axis=axis,
out=out, keepdims=keepdims)

def _sum(a, axis=None, dtype=None, out=None, keepdims=False):
```

```        if m is nomask:
t = filled(target)
else:
t = umath.minimum.reduce(filled(target, minimum_fill_value(target)), axis)
```

```        if m is nomask:
t = filled(target)
else:
t = umath.minimum.reduce(filled(target, minimum_fill_value(target)), axis)
```

```        if m is nomask:
t = filled(target)
else:
t = umath.minimum.reduce(filled(target, minimum_fill_value(target)), axis)
```

```        if m is nomask:
t = filled(target)
else:
t = umath.minimum.reduce(filled(target, minimum_fill_value(target)), axis)
```

```        if m is nomask:
t = filled(target)
```    def check_reduce_complex(self):