Reduction

reduce_sum

def reduce_sum(x: 'Tensor', *, axis: 'int | tuple[int, ...] | list[int] | None' = None, keepdims: 'bool' = False) -> 'Tensor':

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reduce_max

def reduce_max(x: 'Tensor', *, axis: 'int | tuple[int, ...] | list[int] | None' = None, keepdims: 'bool' = False) -> 'Tensor':

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reduce_min

def reduce_min(x: 'Tensor', *, axis: 'int | tuple[int, ...] | list[int] | None' = None, keepdims: 'bool' = False) -> 'Tensor':

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mean

def mean(x: 'Tensor', *, axis: 'int | tuple[int, ...] | list[int] | None' = None, keepdims: 'bool' = False) -> 'Tensor':

Compute arithmetic mean along specified axis/axes.

Implemented as sum(x) / product(shape[axes]) to correctly handle distributed sharding.

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argmax

def argmax(x: 'Tensor', axis: 'int' = -1, keepdims: 'bool' = False) -> 'Tensor':

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argmin

def argmin(x: 'Tensor', axis: 'int' = -1, keepdims: 'bool' = False) -> 'Tensor':

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cumsum

def cumsum(x: 'Tensor', axis: 'int' = -1, exclusive: 'bool' = False, reverse: 'bool' = False) -> 'Tensor':

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reduce_sum_physical

def reduce_sum_physical(x: 'Tensor', axis: 'int', keepdims: 'bool' = False) -> 'Tensor':

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mean_physical

def mean_physical(x: 'Tensor', axis: 'int', keepdims: 'bool' = False) -> 'Tensor':

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