/// | \f$N[\textit{delete}(A,d_1,\dots,d_n)]\f$ | The tensor \f$T\f$, where \f$T\f$ is the input tensor with the `reduction_axes` \f$A\f$ eliminated by taking the maximum element. |
/// \brief Batched max pooling operation, with optional padding and window stride.
///
/// (TODO: add an account of the optional padding to this comment.)
///
/// Max pooling takes as its input a data batch tensor of shape \f$(N,C,d_1,\dots,d_n)\f$ where \f$n > 0\f$, every \f$d_i > 0\f$, and where \f$N\f$ is
/// the batch size, and \f$C > 0\f$ is the number of channels (sometimes called features). The dimensions \f$(d_1,\dots,d_n)\f$ correspond to the shape of
/// an \f$n\f$-dimensional data item in a batch. For example, where \f$n=2\f$, the data may represent a two-dimensional image. It also takes two parameters:
///
/// 1. <i>(the window shape)</i> a size vector \f$(w_1,\dots,w_n)\f$ where every \f$w_i \le d_i\f$; and
/// 2. <i>(the window movement strides, optional)</i> a vector of positive integers \f$(s_1,\dots,s_n)\f$.
///
/// The output has the shape \f$(N,C,d'_1,\dots,d'_n)\f$, where \f$d'_n = \lceil \frac{d_i - w_i + 1}{s_i} \rceil\f$.
///
/// Given an input data batch tensor \f$T_\textit{in}\f$, the output tensor is defined by the equation
/// | \f$N[\textit{delete}(A,d_1,\dots,d_n)]\f$ | The tensor \f$T\f$, where \f$T\f$ is the input tensor with the `reduction_axes` \f$A\f$ eliminated by taking the minimum element. |
/// Takes an input tensor of shape \f$(d_1,\dots,d_n)\f$ and pads by inserting a scalar \f$x\f$ supplied as input, in three possible ways:
///
/// 1. <i>(exterior padding)</i> inserts copies of \f$x\f$ <i>below or above</i> the bounds of existing rows, columns, etc.,
/// 2. <i>(interior padding)</i> inserts copies of \f$x\f$ <i>between</i> rows, columns, etc., or
/// 3. both of the above.
///
/// The number and position of elements to be inserted along a given axis is determined by three parameters:
///
/// 1. <i>(the padding-below sizes)</i> a vector of non-negative integers \f$(p_1,\dots,p_n)\f$,
/// 2. <i>(the padding-above sizes)</i> a vector of non-negative integers \f$(q_1,\dots,q_n)\f$, and
/// 3. <i>(the interior padding sizes)</i> a vector of non-negative integers \f$(r_1,\dots,r_n)\f$.
///
/// The output tensor will have the shape \f$(d'_1,\dots,d'_n)\f$ where \f$d'_i = p_i + (d_i - 1)(r_i + 1) + 1 + q_i\f$ if \f$d_i > 0\f$, and \f$d'_i = p_i + q_i\f$ if \f$d_i = 0\f$.
///
/// Example: given a 3x3 tensor, with interior-padding sizes of `{1,2}`, padding-below of `{1,2}`, padding-above of `{1,0}`, and a pad-value of `42`, we obtain:
///
/// ```
/// 42 42 42 42 42 42 42 42 42
/// 42 42 1 42 42 2 42 42 3
/// 1 2 3 42 42 42 42 42 42 42 42 42
/// 4 5 6 --> 42 42 4 42 42 5 42 42 6
/// 7 8 9 42 42 42 42 42 42 42 42 42
/// 42 42 7 42 42 8 42 42 9
/// 42 42 42 42 42 42 42 42 42
/// ```
///
/// In other words we have inserted one new row between each pair of adjacent rows, two new columns between each pair of adjacent columns, one new row at
/// the top and two new columns on the left, and one new row at the bottom and zero new columns on the right; then filled the new rows and columns with `42`.
///
/// (Note that `below` and `above` here refer respectively to lower- or higher-numbered coordinate indices, and numbering starts at the upper-left corner;
/// thus inserting a row "below" actually inserts it at the "top" of the matrix.)
