function maxPool3d_(x, filterSize = [
    1,
    1,
    1
], strides, pad, dimRoundingMode, dataFormat = "NDHWC") {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "maxPool3d");
    let x5D = $x;
    let reshapedTo5D = false;
    if ($x.rank === 4) {
        reshapedTo5D = true;
        x5D = (0, _reshape.reshape)($x, [
            1,
            $x.shape[0],
            $x.shape[1],
            $x.shape[2],
            $x.shape[3]
        ]);
    }
    _util.assert(x5D.rank === 5, ()=>`Error in maxPool3d: x must be rank 5 but got rank ${x5D.rank}.`);
    _util.assert(dataFormat === "NDHWC", ()=>`Error in maxPool3d: Only NDHWC is currently supported, ` + `but got dataFormat of ${dataFormat}`);
    (0, _convUtil.checkPadOnDimRoundingMode)("maxPool3d", pad, dimRoundingMode);
    const inputs = {
        x: x5D
    };
    const attrs = {
        filterSize,
        strides,
        pad,
        dimRoundingMode,
        dataFormat
    };
    // tslint:disable-next-line: no-unnecessary-type-assertion
    const res = (0, _engine.ENGINE).runKernel((0, _kernelNames.MaxPool3D), inputs, attrs);
    if (reshapedTo5D) return (0, _reshape.reshape)(res, [
        res.shape[1],
        res.shape[2],
        res.shape[3],
        res.shape[4]
    ]);
    return res;
}
Computes the 3D max pooling.

```js
const x = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);
const result = tf.maxPool3d(x, 2, 1, 'valid');
result.print();
```

@param x The input tensor, of rank 5 or rank 4 of shape
    `[batch, depth, height, width, inChannels]`.
@param filterSize The filter size:
    `[filterDepth, filterHeight, filterWidth]`.
    If `filterSize` is a single number,
    then `filterDepth == filterHeight == filterWidth`.
@param strides The strides of the pooling:
    `[strideDepth, strideHeight, strideWidth]`.
    If `strides` is a single number,
    then `strideDepth == strideHeight == strideWidth`.
@param pad The type of padding algorithm.
   - `same` and stride 1: output will be of same size as input,
      regardless of filter size.
   - `valid`: output will be smaller than input if filter is larger
      than 1*1x1.
   - For more info, see this guide:
    [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](
         https://www.tensorflow.org/api_docs/python/tf/nn/convolution)
@param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is
    provided, it will default to truncate.
@param dataFormat An optional string from: "NDHWC", "NCDHW". Defaults to
    "NDHWC". Specify the data format of the input and output data. With the
    default format "NDHWC", the data is stored in the order of: [batch,
    depth, height, width, channels]. Only "NDHWC" is currently supported.
@doc {heading: 'Operations', subheading: 'Convolution'}