Function min

imperative/python/megengine/functional/math.py:399–437

r"""Calculates the minimum of tensor elements over a given axis (or axes). Args: inp: input tensor. Should have a numeric data type. axis: axis or axes along which minimums must be computed. By default, the minimum must be computed over the entire tensor.

(
    inp: Tensor,
    axis: Optional[Union[int, Sequence[int]]] = None,
    keepdims: bool = False,
)

def min(
    inp: Tensor,
    axis: Optional[Union[int, Sequence[int]]] = None,
    keepdims: bool = False,
) -> Tensor:
    r"""Calculates the minimum of tensor elements over a given axis (or axes).

    Args:
        inp: input tensor. Should have a numeric data type.
        axis: axis or axes along which minimums must be computed.
            By default, the minimum must be computed over the entire tensor.
            If a sequence of integers, minimums must be computed over multiple axes.
        keepdims: if ``True``, the reduced axes (dimensions) must be included in the result as singleton dimensions,
            and, accordingly, the result must be compatible with the input tensor (see :ref:`broadcasting-rule`).
            Otherwise, if ``False``, the reduced axes (dimensions) must not be included in the result.
    
    Returns:
        if the minimum was computed over the entire tensor, a zero-dimensional tensor containing the minimum;
        otherwise, a non-zero-dimensional tensor containing the minimums.

    .. admonition:: Special Cases

       If :math:`x_i` is ``NaN``, the minimum is ``NaN`` (i.e., ``NaN`` values propagate).

    Examples:

        >>> x = Tensor([[1, 2], [3, 4]])
        >>> F.min(x)
        Tensor(1, dtype=int32, device=xpux:0)

        Along an axis:

        >>> F.min(x, axis=0)
        Tensor([1 2], dtype=int32, device=xpux:0)
        >>> F.min(x, axis=1)
        Tensor([1 3], dtype=int32, device=xpux:0)

    """
    return inp.min(axis=axis, keepdims=keepdims)


def max(