```python
import torch
import torch.nn as nn
from torch.nn.modules.utils import _triple
from torch.utils.cpp_extension import load_inline


_cpp_source = r"""
#include <torch/extension.h>
#include <vector>

std::vector<torch::Tensor> maxpool3d_cuda(
    torch::Tensor x,
    std::vector<int64_t> kernel,
    std::vector<int64_t> stride,
    std::vector<int64_t> padding,
    std::vector<int64_t> dilation,
    bool ceil_mode,
    bool return_indices);
"""

_cuda_source = r"""
#include <torch/extension.h>
#include <ATen/AccumulateType.h>
#include <c10/cuda/CUDAGuard.h>
#include <c10/cuda/CUDAStream.h>
#include <c10/cuda/CUDAException.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <cmath>
#include <vector>

template <typename scalar_t, bool ReturnIndices, bool Specialized>
__global__ void maxpool3d_kernel(
    const scalar_t* __restrict__ input,
    scalar_t* __restrict__ output,
    int64_t* __restrict__ indices,
    int64_t count,
    int D, int H, int W,
    int OD, int OH, int OW,
    int KD, int KH, int KW,
    int SD, int SH, int SW,
    int PD, int PH, int PW,
    int DD, int DH, int DW)
{
    using acc_t = at::acc_type<scalar_t, true>;

    const int kd = Specialized ? 3 : KD;
    const int kh = Specialized ? 3 : KH;
    const int kw = Specialized ? 3 : KW;
    const int sd = Specialized ? 2 : SD;
    const int sh = Specialized ? 2 : SH;
    const int sw = Specialized ? 2 : SW;
    const int pd = Specialized ? 1 : PD;
    const int ph = Specialized ? 1 : PH;
    const int pw = Specialized ? 1 : PW;
    const int dd = Specialized ? 3 : DD;
    const int dh = Specialized ? 3 : DH;
    const int dw = Specialized ? 3 : DW;

    const int64_t input_volume = int64_t(D) * H * W;
    const int64_t output_volume = int64_t(OD) * OH * OW;

    for (int64_t i = int64_t(blockIdx.x) * blockDim.x + threadIdx.x;
         i < count;
         i += int64_t(blockDim.x) * gridDim.x)
    {
        const int64_t plane = i / output_volume;
        const int spatial = int(i - plane * output_volume);
        const int ow = spatial % OW;
        const int oh = (spatial / OW) % OH;
        const int od = spatial / (OW * OH);

        const int z0 = od * sd - pd;
        const int y0 = oh * sh - ph;
        const int x0 = ow * sw - pw;

        const scalar_t* base = input + plane * input_volume;
        acc_t best = -INFINITY;
        int64_t best_index = 0;

        if (ReturnIndices) {
            const int first_z = z0 < 0 ? z0 + ((-z0 + dd - 1) / dd) * dd : z0;
            const int first_y = y0 < 0 ? y0 + ((-y0 + dh - 1) / dh) * dh : y0;
            const int first_x = x0 < 0 ? x0 + ((-x0 + dw - 1) / dw) * dw : x0;
            best_index = (int64_t(first_z) * H + first_y) * W + first_x;
        }

        #pragma unroll
        for (int iz = 0; iz < kd; ++iz) {
            const int z = z0 + iz * dd;
            if (static_cast<unsigned>(z) >= static_cast<unsigned>(D))
                continue;

            #pragma unroll
            for (int iy = 0; iy < kh; ++iy) {
                const int y = y0 + iy * dh;
                if (static_cast<unsigned>(y) >= static_cast<unsigned>(H))
                    continue;

                const int64_t row = (int64_t(z) * H + y) * W;

                #pragma unroll
                for (int ix = 0; ix < kw; ++ix) {
                    const int x = x0 + ix * dw;
                    if (static_cast<unsigned>(x) < static_cast<unsigned>(W)) {
                        const acc_t value = static_cast<acc_t>(base[row + x]);
                        if (value > best || isnan(value)) {
                            best = value;
                            if (ReturnIndices)
                                best_index = row + x;
                        }
                    }
                }
            }
        }

        output[i] = static_cast<scalar_t>(best);
        if (ReturnIndices)
            indices[i] = best_index;
    }
}

static int64_t output_size(
    int64_t input, int64_t kernel, int64_t stride,
    int64_t padding, int64_t dilation, bool ceil_mode)
{
    const int64_t numerator =
        input + 2 * padding - dilation * (kernel - 1) - 1
        + (ceil_mode ? stride - 1 : 0);

    int64_t result = numerator / stride;
    if (numerator < 0 && numerator % stride != 0)
        --result;
    ++result;

    if (ceil_mode && (result - 1) * stride >= input + padding)
        --result;

    return result;
}

std::vector<torch::Tensor> maxpool3d_cuda(
    torch::Tensor x,
    std::vector<int64_t> kernel,
    std::vector<int64_t> stride,
    std::vector<int64_t> padding,
    std::vector<int64_t> dilation,
    bool ceil_mode,
    bool return_indices)
{
    TORCH_CHECK(x.is_cuda() && x.is_contiguous(),
                "Expected a contiguous CUDA tensor");
    TORCH_CHECK(x.dim() == 4 || x.dim() == 5,
                "Expected a 4D or 5D tensor");
    TORCH_CHECK(kernel.size() == 3 && stride.size() == 3 &&
                padding.size() == 3 && dilation.size() == 3,
                "Pooling parameters must have three elements");

    const c10::cuda::CUDAGuard guard(x.device());

    const int rank = x.dim();
    const int64_t D = x.size(rank - 3);
    const int64_t H = x.size(rank - 2);
    const int64_t W = x.size(rank - 1);

    TORCH_CHECK(x.size(rank - 4) > 0 && D > 0 && H > 0 && W > 0,
                "Channels and spatial dimensions must be nonzero");

    for (int j = 0; j < 3; ++j) {
        TORCH_CHECK(kernel[j] > 0 && stride[j] > 0 && dilation[j] > 0,
                    "Kernel, stride, and dilation must be positive");
        TORCH_CHECK(padding[j] >= 0 && padding[j] <= kernel[j] / 2,
                    "Padding must be between zero and half the kernel size");
    }

    const int64_t OD = output_size(
        D, kernel[0], stride[0], padding[0], dilation[0], ceil_mode);
    const int64_t OH = output_size(
        H, kernel[1], stride[1], padding[1], dilation[1], ceil_mode);
    const int64_t OW = output_size(
        W, kernel[2], stride[2], padding[2], dilation[2], ceil_mode);

    TORCH_CHECK(OD > 0 && OH > 0 && OW > 0,
                "Calculated output spatial dimensions must be positive");

    auto shape = x.sizes().vec();
    shape[rank - 3] = OD;
    shape[rank - 2] = OH;
    shape[rank - 1] = OW;

    auto output = torch::empty(shape, x.options());
    torch::Tensor indices;
    if (return_indices)
        indices = torch::empty(shape, x.options().dtype(torch::kLong));

    const int64_t count = output.numel();
    if (count == 0) {
        if (return_indices)
            return {output, indices};
        return {output};
    }

    const bool specialized =
        kernel[0] == 3 && kernel[1] == 3 && kernel[2] == 3 &&
        stride[0] == 2 && stride[1] == 2 && stride[2] == 2 &&
        padding[0] == 1 && padding[1] == 1 && padding[2] == 1 &&
        dilation[0] == 3 && dilation[1] == 3 && dilation[2] == 3;

    constexpr int threads = 256;
    const int blocks = static_cast<int>(
        std::min<int64_t>((count + threads - 1) / threads, 65535));
    const cudaStream_t stream =
        c10::cuda::getCurrentCUDAStream(x.get_device());

    AT_DISPATCH_FLOATING_TYPES_AND2(
        at::ScalarType::Half, at::ScalarType::BFloat16,
        x.scalar_type(), "custom_maxpool3d", [&] {
            const scalar_t* in = x.data_ptr<scalar_t>();
            scalar_t* out = output.data_ptr<scalar_t>();
            int64_t* idx = return_indices ? indices.data_ptr<int64_t>() : nullptr;

            #define LAUNCH(INDICES, SPECIALIZED) \
                maxpool3d_kernel<scalar_t, INDICES, SPECIALIZED> \
                <<<blocks, threads, 0, stream>>>( \
                    in, out, idx, count, D, H, W, OD, OH, OW, \
                    kernel[0], kernel[1], kernel[2], \
                    stride[0], stride[1], stride[2], \
                    padding[0], padding[1], padding[2], \
                    dilation[0], dilation[1], dilation[2])

            if (return_indices) {
                if (specialized) {
                    LAUNCH(true, true);
                } else {
                    LAUNCH(true, false);
                }
            } else {
                if (specialized) {
                    LAUNCH(false, true);
                } else {
                    LAUNCH(false, false);
                }
            }
            #undef LAUNCH
        });

    C10_CUDA_KERNEL_LAUNCH_CHECK();

    if (return_indices)
        return {output, indices};
    return {output};
}
"""

_extension = load_inline(
    name="custom_maxpool3d_dilated_v1",
    cpp_sources=_cpp_source,
    cuda_sources=_cuda_source,
    functions=["maxpool3d_cuda"],
    extra_cflags=["-O3"],
    extra_cuda_cflags=["-O3"],
    verbose=False,
)


class ModelNew(nn.Module):
    def __init__(
        self,
        kernel_size: int,
        stride: int = None,
        padding: int = 0,
        dilation: int = 1,
        return_indices: bool = False,
        ceil_mode: bool = False,
    ):
        super().__init__()
        self.kernel_size = tuple(_triple(kernel_size))
        self.stride = tuple(_triple(kernel_size if stride is None else stride))
        self.padding = tuple(_triple(padding))
        self.dilation = tuple(_triple(dilation))
        self.return_indices = return_indices
        self.ceil_mode = ceil_mode
        self.maxpool = nn.MaxPool3d(
            kernel_size=kernel_size,
            stride=stride,
            padding=padding,
            dilation=dilation,
            return_indices=return_indices,
            ceil_mode=ceil_mode,
        )

    def forward(self, x: torch.Tensor):
        if (
            not x.is_cuda
            or x.dtype not in (
                torch.float16,
                torch.bfloat16,
                torch.float32,
                torch.float64,
            )
            or (torch.is_grad_enabled() and x.requires_grad)
        ):
            return self.maxpool(x)

        results = _extension.maxpool3d_cuda(
            x.contiguous(),
            self.kernel_size,
            self.stride,
            self.padding,
            self.dilation,
            self.ceil_mode,
            self.return_indices,
        )
        return (results[0], results[1]) if self.return_indices else results[0]
```