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

cuda_src = r"""
#include <torch/extension.h>
#include <ATen/cuda/CUDAContext.h>
#include <cuda_runtime.h>

#define TCO 16

// Fast path: valid when dilation % stride == 0 per dim and dilation_w == stride_w.
// Output o is nonzero only when (o + pad) % stride == 0 for every dim.
// With r = dil/stride, m = (o + pad)/stride: out[m] = sum_k w[k] * x[m - k*r].
// KW is a compile-time constant so the sliding-window register array stays in registers.
template <int KW>
__global__ void __launch_bounds__(256, 2) tconv3d_reduced_kernel(
    const float* __restrict__ x,   // [B, Ci, D, H, W]
    const float* __restrict__ wt,  // [Kd*Kh*KW, Ci, Co] (co contiguous)
    const float* __restrict__ bias,// [Co] or null
    float* __restrict__ out,       // [B, Co, Od, Oh, Ow]
    int B, int Ci, int Co,
    int D, int H, int W,
    int Od, int Oh, int Ow,
    int Kd, int Kh,
    int sd, int sh, int sw,
    int pd, int ph, int pw,
    int rd, int rh,
    int md0, int mdn, int mh0, int mhn, int mw0, int mwn,
    int ndb)
{
    const int tid = threadIdx.x;
    const int mw_grp = tid & 7;          // 8 groups x 4 voxels = 32
    const int mh_l   = (tid >> 3) & 7;   // 8
    const int md_l   = tid >> 6;         // 4

    const int mw_base = mw0 + mw_grp * 4;
    const int mh = mh0 + blockIdx.y * 8 + mh_l;
    const int md = md0 + (int)(blockIdx.z % ndb) * 4 + md_l;
    const int b  = (int)(blockIdx.z / ndb);

    const bool mh_ok = (mh < mh0 + mhn);
    const bool md_ok = (md < md0 + mdn);

    const long long x_batch = (long long)b * Ci * D * H * W;
    const long long o_batch = (long long)b * Co * Od * Oh * Ow;

    const int od = sd * md - pd;
    const int oh = sh * mh - ph;

    #pragma unroll 1
    for (int co0 = 0; co0 < Co; co0 += TCO) {
        float acc[TCO][4];
        #pragma unroll
        for (int c = 0; c < TCO; ++c) {
            #pragma unroll
            for (int v = 0; v < 4; ++v) acc[c][v] = 0.f;
        }

        #pragma unroll 1
        for (int kd = 0; kd < Kd; ++kd) {
            const int id = md - kd * rd;
            const bool id_ok = md_ok && (id >= 0) && (id < D);
            #pragma unroll 1
            for (int kh = 0; kh < Kh; ++kh) {
                const int ih = mh - kh * rh;
                const bool rc_ok = id_ok && mh_ok && (ih >= 0) && (ih < H);
                const float* xp = x + x_batch + ((long long)id * H + ih) * W;
                const float* wp = wt + ((long long)(kd * Kh + kh) * KW) * Ci * Co + co0;
                #pragma unroll 1
                for (int ci = 0; ci < Ci; ++ci) {
                    float xv[KW + 3];
                    if (rc_ok) {
                        const float* xr = xp + (long long)ci * D * H * W;
                        #pragma unroll
                        for (int j = 0; j < KW + 3; ++j) {
                            const int iw = mw_base - (KW - 1) + j;  // rw == 1
                            xv[j] = (iw >= 0 && iw < W) ? __ldg(xr + iw) : 0.f;
                        }
                    } else {
                        #pragma unroll
                        for (int j = 0; j < KW + 3; ++j) xv[j] = 0.f;
                    }
                    #pragma unroll
                    for (int kw = 0; kw < KW; ++kw) {
                        const float* wq = wp + ((long long)kw * Ci + ci) * Co;
                        #pragma unroll
                        for (int c = 0; c < TCO; ++c) {
                            const float wv = __ldg(wq + c);
                            #pragma unroll
                            for (int v = 0; v < 4; ++v) {
                                acc[c][v] = fmaf(xv[v + (KW - 1 - kw)], wv, acc[c][v]);
                            }
                        }
                    }
                }
            }
        }

        #pragma unroll
        for (int c = 0; c < TCO; ++c) {
            const float bv = (bias != nullptr) ? __ldg(bias + co0 + c) : 0.f;
            #pragma unroll
            for (int v = 0; v < 4; ++v) {
                const int mw = mw_base + v;
                if (mw < mw0 + mwn && md_ok && mh_ok) {
                    const int ow = sw * mw - pw;
                    out[o_batch + (((long long)(co0 + c) * Od + od) * Oh + oh) * Ow + ow] = acc[c][v] + bv;
                }
            }
        }
    }
}

// General fallback: handles any stride/padding/dilation combination.
__global__ void tconv3d_general_kernel(
    const float* __restrict__ x,
    const float* __restrict__ wt,   // [Ci, Co, Kd, Kh, Kw]
    const float* __restrict__ bias,
    float* __restrict__ out,
    int B, int Ci, int Co,
    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,
    long long total)
{
    const long long idx = (long long)blockIdx.x * blockDim.x + threadIdx.x;
    if (idx >= total) return;

    const int ow = (int)(idx % Ow);
    const long long t1 = idx / Ow;
    const int oh = (int)(t1 % Oh);
    const long long t2 = t1 / Oh;
    const int od = (int)(t2 % Od);
    const long long t3 = t2 / Od;
    const int co = (int)(t3 % Co);
    const int b  = (int)(t3 / Co);

    const float* xb = x + (long long)b * Ci * D * H * W;
    const int nK = Kd * Kh * Kw;

    float acc = 0.f;
    for (int ci = 0; ci < Ci; ++ci) {
        const float* xc = xb + (long long)ci * D * H * W;
        const float* wc = wt + (long long)ci * Co * nK + (long long)co * nK;
        int kk = 0;
        for (int kd = 0; kd < Kd; ++kd) {
            const int tmpd = od + pd - kd * dd;
            if (tmpd % sd != 0) { kk += Kh * Kw; continue; }
            const int id = tmpd / sd;
            if (id < 0 || id >= D) { kk += Kh * Kw; continue; }
            for (int kh = 0; kh < Kh; ++kh) {
                const int tmph = oh + ph - kh * dh;
                if (tmph % sh != 0) { kk += Kw; continue; }
                const int ih = tmph / sh;
                if (ih < 0 || ih >= H) { kk += Kw; continue; }
                for (int kw = 0; kw < Kw; ++kw, ++kk) {
                    const int tmpw = ow + pw - kw * dw;
                    if (tmpw % sw != 0) continue;
                    const int iw = tmpw / sw;
                    if (iw < 0 || iw >= W) continue;
                    acc = fmaf(__ldg(xc + ((long long)id * H + ih) * W + iw),
                               __ldg(wc + kk), acc);
                }
            }
        }
    }
    if (bias != nullptr) acc += __ldg(bias + co);
    out[idx] = acc;
}

torch::Tensor tconv3d_forward(
    torch::Tensor x, torch::Tensor w, torch::Tensor bias,
    int64_t sd, int64_t sh, int64_t sw,
    int64_t pd, int64_t ph, int64_t pw,
    int64_t dd, int64_t dh, int64_t dw,
    bool weight_transposed)
{
    TORCH_CHECK(x.is_cuda() && w.is_cuda(), "inputs must be CUDA");
    TORCH_CHECK(x.scalar_type() == at::kFloat, "only float32 supported");

    auto xc = x.contiguous();
    auto wc = w.contiguous();

    const int B  = (int)xc.size(0);
    const int Ci = (int)xc.size(1);
    const int D  = (int)xc.size(2);
    const int H  = (int)xc.size(3);
    const int W  = (int)xc.size(4);

    int Co, Kd, Kh, Kw;
    if (weight_transposed) {
        Kd = (int)wc.size(0); Kh = (int)wc.size(1); Kw = (int)wc.size(2);
        Co = (int)wc.size(4);
    } else {
        Co = (int)wc.size(1);
        Kd = (int)wc.size(2); Kh = (int)wc.size(3); Kw = (int)wc.size(4);
    }

    const int64_t Od = (D - 1) * sd - 2 * pd + dd * (Kd - 1) + 1;
    const int64_t Oh = (H - 1) * sh - 2 * ph + dh * (Kh - 1) + 1;
    const int64_t Ow = (W - 1) * sw - 2 * pw + dw * (Kw - 1) + 1;

    auto out = torch::empty({B, Co, Od, Oh, Ow}, xc.options());
    if (out.numel() == 0) return out;

    const bool has_bias = bias.defined() && bias.numel() > 0;
    cudaStream_t stream = at::cuda::getCurrentCUDAStream();

    bool fast = weight_transposed && (Co % TCO == 0) &&
                (dd % sd == 0) && (dh % sh == 0) && (dw == sw) &&
                Kw >= 1 && Kw <= 5;

    if (weight_transposed && !fast) {
        // recover original layout [Ci, Co, Kd, Kh, Kw]
        wc = wc.permute({3, 4, 0, 1, 2}).contiguous();
    }

    if (fast) {
        const int rd = (int)(dd / sd), rh = (int)(dh / sh);
        const int md0 = (int)((pd + sd - 1) / sd);
        const int mdn = (int)((Od - 1 + pd) / sd) - md0 + 1;
        const int mh0 = (int)((ph + sh - 1) / sh);
        const int mhn = (int)((Oh - 1 + ph) / sh) - mh0 + 1;
        const int mw0 = (int)((pw + sw - 1) / sw);
        const int mwn = (int)((Ow - 1 + pw) / sw) - mw0 + 1;

        if (sd > 1 || sh > 1 || sw > 1) {
            cudaMemsetAsync(out.data_ptr(), 0, (size_t)out.numel() * sizeof(float), stream);
        }
        if (mdn <= 0 || mhn <= 0 || mwn <= 0) return out;

        const int ndb = (mdn + 3) / 4;
        dim3 block(256);
        dim3 grid((unsigned)((mwn + 31) / 32), (unsigned)((mhn + 7) / 8), (unsigned)(B * ndb));

        const float* xp = xc.data_ptr<float>();
        const float* wp = wc.data_ptr<float>();
        const float* bp = has_bias ? bias.data_ptr<float>() : nullptr;
        float* op = out.data_ptr<float>();

        #define LAUNCH(K) tconv3d_reduced_kernel<K><<<grid, block, 0, stream>>>( \
            xp, wp, bp, op, B, Ci, Co, D, H, W, (int)Od, (int)Oh, (int)Ow, Kd, Kh, \
            (int)sd, (int)sh, (int)sw, (int)pd, (int)ph, (int)pw, rd, rh, \
            md0, mdn, mh0, mhn, mw0, mwn, ndb)
        switch (Kw) {
            case 1: LAUNCH(1); break;
            case 2: LAUNCH(2); break;
            case 3: LAUNCH(3); break;
            case 4: LAUNCH(4); break;
            default: LAUNCH(5); break;
        }
        #undef LAUNCH
    } else {
        const long long total = (long long)out.numel();
        const int threads = 256;
        const long long blocks = (total + threads - 1) / threads;
        tconv3d_general_kernel<<<(unsigned)blocks, threads, 0, stream>>>(
            xc.data_ptr<float>(), wc.data_ptr<float>(),
            has_bias ? bias.data_ptr<float>() : nullptr,
            out.data_ptr<float>(),
            B, Ci, Co, D, H, W,
            (int)Od, (int)Oh, (int)Ow,
            Kd, Kh, Kw,
            (int)sd, (int)sh, (int)sw,
            (int)pd, (int)ph, (int)pw,
            (int)dd, (int)dh, (int)dw,
            total);
    }
    return out;
}
"""

cpp_src = """
torch::Tensor tconv3d_forward(
    torch::Tensor x, torch::Tensor w, torch::Tensor bias,
    int64_t sd, int64_t sh, int64_t sw,
    int64_t pd, int64_t ph, int64_t pw,
    int64_t dd, int64_t dh, int64_t dw,
    bool weight_transposed);
"""

tconv3d = load_inline(
    name="tconv3d_custom_v2",
    cpp_sources=cpp_src,
    cuda_sources=cuda_src,
    functions=["tconv3d_forward"],
    verbose=False,
    extra_cuda_cflags=["-O3", "--use_fast_math"],
)


class ModelNew(nn.Module):
    def __init__(self, in_channels: int, out_channels: int, kernel_size: int,
                 stride: int = 1, padding: int = 0, dilation: int = 1,
                 bias: bool = False):
        super(ModelNew, self).__init__()
        self.conv_transpose3d = nn.ConvTranspose3d(
            in_channels, out_channels,
            kernel_size=(kernel_size, kernel_size, kernel_size),
            stride=stride, padding=padding, dilation=dilation, bias=bias)
        s = self.conv_transpose3d.stride
        p = self.conv_transpose3d.padding
        d = self.conv_transpose3d.dilation
        self.sd, self.sh, self.sw = int(s[0]), int(s[1]), int(s[2])
        self.pd, self.ph, self.pw = int(p[0]), int(p[1]), int(p[2])
        self.dd, self.dh, self.dw = int(d[0]), int(d[1]), int(d[2])
        self._fast = (self.dd % self.sd == 0 and self.dh % self.sh == 0 and
                      self.dw == self.sw and out_channels % 16 == 0 and
                      1 <= int(kernel_size) <= 5)
        self._wt = None
        self._wt_ver = -1
        self._empty_bias = None

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        if x.dtype != torch.float32 or not x.is_cuda:
            return self.conv_transpose3d(x)
        w = self.conv_transpose3d.weight
        b = self.conv_transpose3d.bias
        if self._fast:
            wt = self._wt
            if wt is None or self._wt_ver != w._version or wt.device != w.device:
                wt = w.detach().permute(2, 3, 4, 0, 1).contiguous()
                self._wt = wt
                self._wt_ver = w._version
            warg = wt
        else:
            warg = w
        if b is None:
            eb = self._empty_bias
            if eb is None or eb.device != x.device:
                eb = torch.empty(0, device=x.device, dtype=x.dtype)
                self._empty_bias = eb
            b = eb
        return tconv3d.tconv3d_forward(
            x, warg, b,
            self.sd, self.sh, self.sw,
            self.pd, self.ph, self.pw,
            self.dd, self.dh, self.dw,
            self._fast)
```