You write custom CUDA kernels to replace the pytorch operators in the given architecture to get speedups. 

    You have complete freedom to choose the set of operators you want to replace. You may make the decision to replace some operators with custom CUDA kernels and leave others unchanged. You may replace multiple operators with custom implementations, consider operator fusion opportunities (combining multiple operators into a single kernel, for example, combining matmul+relu), or algorithmic changes (such as online softmax). You are only limited by your imagination.


        Here's an example to show you the syntax of inline embedding custom CUDA operators in torch: The example given architecture is: 

        ``` 

        # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

import torch
import torch.nn as nn
import torch.nn.functional as F


class Model(nn.Module):
    def __init__(self) -> None:
        super().__init__()

    def forward(self, a, b):
        return a + b


def get_inputs():
    # randomly generate input tensors based on the model architecture
    a = torch.randn(1, 128).cuda()
    b = torch.randn(1, 128).cuda()
    return [a, b]


def get_init_inputs():
    # randomly generate tensors required for initialization based on the model architecture
    return []

        ``` 

        The example new arch with custom CUDA kernels looks like this:
        ```
        # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.cpp_extension import load_inline

# Define the custom CUDA kernel for element-wise addition
elementwise_add_source = """
#include <torch/extension.h>
#include <cuda_runtime.h>

__global__ void elementwise_add_kernel(const float* a, const float* b, float* out, int size) {
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    if (idx < size) {
        out[idx] = a[idx] + b[idx];
    }
}

torch::Tensor elementwise_add_cuda(torch::Tensor a, torch::Tensor b) {
    auto size = a.numel();
    auto out = torch::zeros_like(a);

    const int block_size = 256;
    const int num_blocks = (size + block_size - 1) / block_size;

    elementwise_add_kernel<<<num_blocks, block_size>>>(a.data_ptr<float>(), b.data_ptr<float>(), out.data_ptr<float>(), size);

    return out;
}
"""

elementwise_add_cpp_source = (
    "torch::Tensor elementwise_add_cuda(torch::Tensor a, torch::Tensor b);"
)

# Compile the inline CUDA code for element-wise addition
elementwise_add = load_inline(
    name="elementwise_add",
    cpp_sources=elementwise_add_cpp_source,
    cuda_sources=elementwise_add_source,
    functions=["elementwise_add_cuda"],
    verbose=True,
    extra_cflags=[""],
    extra_ldflags=[""],
)


class ModelNew(nn.Module):
    def __init__(self) -> None:
        super().__init__()
        self.elementwise_add = elementwise_add

    def forward(self, a, b):
        return self.elementwise_add.elementwise_add_cuda(a, b)

        ``` 

        
    You are given the following architecture: 

    ```
    # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

import torch
import torch.nn as nn

class Model(nn.Module):
    """
    Model that performs a transposed convolution, subtracts a bias term, and applies tanh activation.
    """
    def __init__(self, in_channels, out_channels, kernel_size, bias_shape, stride=2, padding=1, output_padding=1):
        super(Model, self).__init__()
        self.conv_transpose = nn.ConvTranspose2d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, output_padding=output_padding)
        self.bias = nn.Parameter(torch.randn(bias_shape)) 

    def forward(self, x):
        x = self.conv_transpose(x)
        x = x - self.bias
        x = torch.tanh(x)
        return x

batch_size = 32
in_channels  = 64  
out_channels = 64  
height = width = 256 
kernel_size = 4
bias_shape = (out_channels, 1, 1)

def get_inputs():
    return [torch.rand(batch_size, in_channels, height, width)]

def get_init_inputs():
    return [in_channels, out_channels, kernel_size, bias_shape]
    ```
    
Optimize the architecture named Model with custom CUDA operators! Name your optimized output architecture ModelNew. Output the new code in codeblocks. Please generate real code, NOT pseudocode, make sure the code compiles and is fully functional. Just output the new model code, no other text, and NO testing code! 



This is turn 5 of 6 for this task. Your previous solution (turn 4) was:
```python
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

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

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

using namespace nvcuda;

// ---------------------------------------------------------------------------
// Fused ConvTranspose2d(k=4,s=2,p=1,op=1) + bias-subtract + tanh.
//
// The transposed convolution is decomposed into 4 parity "phases"
// (po = oh & 1, pq = ow & 1).  For each phase the operation is an exact
// GEMM over output pixels M = (n, oh2, ow2) with K = (ci, a, b):
//
//   out = sum_{ci,a,b} x[n, ci, oh2+po-a, ow2+pq-b] * w[ci, co, 2a+1-po, 2b+1-pq]
//
// computed with a TF32 tensor-core (WMMA) implicit-GEMM kernel with a
// two-stage shared-memory software pipeline, with (+conv_bias, -bias, tanh)
// fused in the epilogue.
// ---------------------------------------------------------------------------

#define TILE_M 128
#define TILE_N 64
#define SLICE_K 16
#define NTHREADS 256
#define LDA (TILE_M + 8)
#define LDB (TILE_N + 8)
#define LDC (TILE_M + 8)

__device__ __forceinline__ float to_tf32(float x) {
    // round-to-nearest (ties up) truncation to tf32 precision
    unsigned int u = __float_as_uint(x);
    u = (u + 0x1000u) & ~0x1FFFu;
    return __uint_as_float(u);
}

// wp[phase][k=(ci,a,b)][co]  with  k = ci*4 + a*2 + b  (tf32-rounded)
// also computes combined bias comb[co] = conv_bias[co] - sub_bias[co]
__global__ void rearrange_weight_kernel(const float* __restrict__ w,
                                        const float* __restrict__ conv_bias,
                                        const float* __restrict__ sub_bias,
                                        float* __restrict__ wp,
                                        float* __restrict__ comb,
                                        int has_cb) {
    if (blockIdx.x == 256) {
        int co = threadIdx.x;
        if (co < 64) {
            float cb = has_cb ? __ldg(conv_bias + co) : 0.0f;
            comb[co] = cb - __ldg(sub_bias + co);
        }
        return;
    }
    int idx = blockIdx.x * blockDim.x + threadIdx.x;   // 4*256*64 elements
    int co = idx & 63;
    int k  = (idx >> 6) & 255;
    int p  = idx >> 14;
    int po = p >> 1, pq = p & 1;
    int ci = k >> 2;
    int a  = (k >> 1) & 1;
    int b  = k & 1;
    int kh = 2 * a + 1 - po;
    int kw = 2 * b + 1 - pq;
    wp[idx] = to_tf32(w[(long)ci * 1024 + co * 16 + kh * 4 + kw]);
}

__global__ void __launch_bounds__(NTHREADS) deconv_fused_tc_kernel(
        const float* __restrict__ x,
        const float* __restrict__ wp,
        const float* __restrict__ comb,
        float* __restrict__ y,
        int N, int H, int W, int Ho, int Wo) {

    __shared__ union {
        struct {
            float As[2][SLICE_K][LDA];
            float Bs[2][SLICE_K][LDB];
        } ab;
        float Cs[TILE_N][LDC];   // transposed accumulator tile [co][m]
    } smem;
    __shared__ int soff[TILE_M][4];
    __shared__ long long sbase[TILE_M];
    __shared__ long long sorow[TILE_M];
    __shared__ float scomb[TILE_N];

    const int phase = blockIdx.z;
    const int po = phase >> 1, pq = phase & 1;
    const int Hp = (Ho - po + 1) >> 1;
    const int Wp2 = (Wo - pq + 1) >> 1;
    const long M = (long)N * Hp * Wp2;
    const long row0 = (long)blockIdx.x * TILE_M;
    if (row0 >= M) return;

    const int tid = threadIdx.x;
    const long xsc = (long)H * W;
    const long xsn = 64 * xsc;
    const long ytc = (long)Ho * Wo;
    const long ytn = 64 * ytc;

    if (tid < TILE_M) {
        long row = row0 + tid;
        if (row < M) {
            long hw = (long)Hp * Wp2;
            long n = row / hw;
            long rem = row - n * hw;
            int oh2 = (int)(rem / Wp2);
            int ow2 = (int)(rem - (long)oh2 * Wp2);
            sbase[tid] = (long long)n * xsn;
            #pragma unroll
            for (int ab = 0; ab < 4; ++ab) {
                int a = ab >> 1, b = ab & 1;
                int ih = oh2 + po - a;
                int iw = ow2 + pq - b;
                bool v = (ih >= 0 && ih < H && iw >= 0 && iw < W);
                soff[tid][ab] = v ? (ih * W + iw) : -1;
            }
            int oh = 2 * oh2 + po;
            int ow = 2 * ow2 + pq;
            sorow[tid] = (long long)n * ytn + (long)oh * Wo + ow;
        } else {
            sbase[tid] = 0;
            #pragma unroll
            for (int ab = 0; ab < 4; ++ab) soff[tid][ab] = -1;
            sorow[tid] = -1;
        }
    }
    if (tid < TILE_N) scomb[tid] = __ldg(comb + tid);
    __syncthreads();

    const int warp = tid >> 5;
    const int wm = warp & 3;    // 4 warps along M (32 rows each)
    const int wn = warp >> 2;   // 2 warps along N (32 cols each)
    const int m_off = wm * 32;
    const int n_off = wn * 32;

    wmma::fragment<wmma::accumulator, 16, 16, 8, float> cfrag[2][2];
    #pragma unroll
    for (int i = 0; i < 2; ++i)
        #pragma unroll
        for (int j = 0; j < 2; ++j)
            wmma::fill_fragment(cfrag[i][j], 0.0f);

    const float* wphase = wp + (long)phase * (256 * 64);

    // stage k-slice `ks` (16 k-values) into buffer `buf`
    auto stage = [&](int ks, int buf) {
        int kbase = ks * SLICE_K;
        #pragma unroll
        for (int it = 0; it < (SLICE_K * TILE_M) / NTHREADS; ++it) {  // 8
            int idx = tid + it * NTHREADS;
            int m  = idx & (TILE_M - 1);
            int kk = idx >> 7;
            int kg = kbase + kk;
            int ci = kg >> 2;
            int ab = kg & 3;
            int off = soff[m][ab];
            float v = 0.0f;
            if (off >= 0) v = __ldg(x + sbase[m] + (long)ci * xsc + off);
            smem.ab.As[buf][kk][m] = to_tf32(v);
        }
        #pragma unroll
        for (int it = 0; it < (SLICE_K * TILE_N) / NTHREADS; ++it) {  // 4
            int idx = tid + it * NTHREADS;
            int co = idx & 63;
            int kk = idx >> 6;
            smem.ab.Bs[buf][kk][co] = __ldg(wphase + (long)(kbase + kk) * 64 + co);
        }
    };

    stage(0, 0);
    __syncthreads();

    #pragma unroll
    for (int ks = 0; ks < 256 / SLICE_K; ++ks) {
        int buf = ks & 1;
        if (ks + 1 < 256 / SLICE_K) stage(ks + 1, buf ^ 1);
        #pragma unroll
        for (int kk = 0; kk < SLICE_K / 8; ++kk) {
            wmma::fragment<wmma::matrix_a, 16, 16, 8, wmma::precision::tf32, wmma::col_major> afrag[2];
            wmma::load_matrix_sync(afrag[0], &smem.ab.As[buf][kk * 8][m_off], LDA);
            wmma::load_matrix_sync(afrag[1], &smem.ab.As[buf][kk * 8][m_off + 16], LDA);
            #pragma unroll
            for (int j = 0; j < 2; ++j) {
                wmma::fragment<wmma::matrix_b, 16, 16, 8, wmma::precision::tf32, wmma::row_major> bfrag;
                wmma::load_matrix_sync(bfrag, &smem.ab.Bs[buf][kk * 8][n_off + j * 16], LDB);
                wmma::mma_sync(cfrag[0][j], afrag[0], bfrag, cfrag[0][j]);
                wmma::mma_sync(cfrag[1][j], afrag[1], bfrag, cfrag[1][j]);
            }
        }
        __syncthreads();
    }

    // write accumulators to shared memory (transposed: [co][m])
    #pragma unroll
    for (int i = 0; i < 2; ++i)
        #pragma unroll
        for (int j = 0; j < 2; ++j)
            wmma::store_matrix_sync(&smem.Cs[n_off + j * 16][m_off + i * 16],
                                    cfrag[i][j], LDC, wmma::mem_col_major);
    __syncthreads();

    // fused epilogue: + combined bias, tanh, write out (m-fastest for coalescing)
    const int me = tid & (TILE_M - 1);
    const int cg = tid >> 7;   // 0 or 1
    const long long orow = sorow[me];
    if (orow >= 0) {
        #pragma unroll
        for (int it = 0; it < (TILE_M * TILE_N) / NTHREADS; ++it) {  // 32
            int co = 2 * it + cg;
            float v = smem.Cs[co][me] + scomb[co];
            y[orow + (long long)co * ytc] = tanhf(v);
        }
    }
}

// Fallback fused elementwise: y = tanh(x - sub_bias) per channel
__global__ void fused_bias_tanh_kernel(const float* __restrict__ x,
                                       const float* __restrict__ sub_bias,
                                       float* __restrict__ y,
                                       long HW, long total, int CO) {
    long idx = (long)blockIdx.x * blockDim.x + threadIdx.x;
    if (idx >= total) return;
    int co = (int)((idx / HW) % CO);
    y[idx] = tanhf(x[idx] - __ldg(sub_bias + co));
}

torch::Tensor deconv_forward(torch::Tensor x, torch::Tensor weight,
                             c10::optional<torch::Tensor> conv_bias,
                             torch::Tensor sub_bias,
                             int64_t stride, int64_t padding,
                             int64_t output_padding) {
    TORCH_CHECK(x.is_cuda() && x.dtype() == torch::kFloat32, "x must be CUDA fp32");
    auto xc = x.contiguous();
    auto wc = weight.contiguous();
    auto bc = sub_bias.contiguous();

    const int N = xc.size(0);
    const int CI = xc.size(1);
    const int H = xc.size(2);
    const int W = xc.size(3);
    const int CO = wc.size(1);
    const int KH = wc.size(2);
    const int KW = wc.size(3);

    const long Ho = (long)(H - 1) * stride - 2 * padding + KH + output_padding;
    const long Wo = (long)(W - 1) * stride - 2 * padding + KW + output_padding;

    auto stream = at::cuda::getCurrentCUDAStream();

    torch::Tensor cb;
    const float* cb_ptr = nullptr;
    int has_cb = 0;
    if (conv_bias.has_value() && conv_bias->defined()) {
        cb = conv_bias->contiguous();
        cb_ptr = cb.data_ptr<float>();
        has_cb = 1;
    }

    auto y = torch::empty({N, CO, Ho, Wo}, xc.options());

    bool fast = (CI == 64) && (CO == 64) && (KH == 4) && (KW == 4) &&
                (stride == 2) && (padding == 1) && (output_padding == 1) &&
                (Ho <= INT_MAX) && (Wo <= INT_MAX);

    if (!fast) {
        // safe fallback: cuDNN/ATen conv (with its bias) + one fused kernel
        auto tmp = at::conv_transpose2d(xc, wc, conv_bias.has_value() ? cb : torch::Tensor(),
                                        {stride, stride}, {padding, padding},
                                        {output_padding, output_padding}, 1);
        long total = tmp.numel();
        long HW = Ho * Wo;
        int threads = 256;
        long blocks = (total + threads - 1) / threads;
        fused_bias_tanh_kernel<<<(unsigned)blocks, threads, 0, stream>>>(
            tmp.data_ptr<float>(), bc.data_ptr<float>(), y.data_ptr<float>(),
            HW, total, CO);
        return y;
    }

    // rearranged weights (tf32-rounded) + combined bias
    auto wp = torch::empty({4 * 256 * 64}, xc.options());
    auto comb = torch::empty({64}, xc.options());
    rearrange_weight_kernel<<<257, 256, 0, stream>>>(
        wc.data_ptr<float>(), cb_ptr, bc.data_ptr<float>(),
        wp.data_ptr<float>(), comb.data_ptr<float>(), has_cb);

    long maxM = 0;
    for (int p = 0; p < 4; ++p) {
        int po = p >> 1, pq = p & 1;
        long Hp = (Ho - po + 1) >> 1;
        long Wp2 = (Wo - pq + 1) >> 1;
        long Mp = (long)N * Hp * Wp2;
        if (Mp > maxM) maxM = Mp;
    }
    long tiles = (maxM + TILE_M - 1) / TILE_M;

    dim3 grid((unsigned)tiles, 1, 4);
    deconv_fused_tc_kernel<<<grid, NTHREADS, 0, stream>>>(
        xc.data_ptr<float>(), wp.data_ptr<float>(), comb.data_ptr<float>(),
        y.data_ptr<float>(), N, H, W, (int)Ho, (int)Wo);
    return y;
}
"""

cpp_source = """
torch::Tensor deconv_forward(torch::Tensor x, torch::Tensor weight,
                             c10::optional<torch::Tensor> conv_bias,
                             torch::Tensor sub_bias,
                             int64_t stride, int64_t padding,
                             int64_t output_padding);
"""

deconv_mod = load_inline(
    name="deconv_fused_tf32_tc",
    cpp_sources=cpp_source,
    cuda_sources=cuda_source,
    functions=["deconv_forward"],
    verbose=False,
    extra_cuda_cflags=["-O3", "--use_fast_math"],
)


class ModelNew(nn.Module):
    """
    Optimized model: transposed convolution via a fused TF32 tensor-core
    implicit-GEMM CUDA kernel with (+conv_bias, -bias, tanh) fused in the
    epilogue.  Falls back to ATen conv_transpose2d + a fused elementwise
    kernel for non-standard configurations.
    """
    def __init__(self, in_channels, out_channels, kernel_size, bias_shape, stride=2, padding=1, output_padding=1):
        super(ModelNew, self).__init__()
        self.conv_transpose = nn.ConvTranspose2d(in_channels, out_channels, kernel_size,
                                                 stride=stride, padding=padding,
                                                 output_padding=output_padding)
        self.bias = nn.Parameter(torch.randn(bias_shape))
        self.stride = stride
        self.padding = padding
        self.output_padding = output_padding

    def forward(self, x):
        return deconv_mod.deconv_forward(
            x,
            self.conv_transpose.weight,
            self.conv_transpose.bias,
            self.bias.view(-1),
            self.stride,
            self.padding,
            self.output_padding,
        )
```
Result of evaluating it:
The solution is correct on the test inputs. Mean runtime 39.2 ms; the PyTorch reference (with TF32 enabled) takes 18.6 ms; speedup 0.474x.
Profiler, top device kernels of one forward pass of your solution:
| device kernel | total device time (us) | calls |
|---|---|---|
| deconv_fused_tc_kernel(float const*, float const*, float const*, float*, int, int, int, in | 39197.1 | 1 |
| rearrange_weight_kernel(float const*, float const*, float const*, float*, float*, int) | 3.0 | 1 |

The fastest correct solution so far (0.610x speedup) is:
```python
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

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

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

// ---------------------------------------------------------------------------
// Fused ConvTranspose2d(k=4,s=2,p=1,op=1) + bias-subtract + tanh.
//
// The transposed convolution is decomposed into 4 parity "phases"
// (po = oh & 1, pq = ow & 1).  For each phase the operation is an exact
// GEMM over output pixels M = (n, oh2, ow2) with K = (ci, a, b):
//
//   out = sum_{ci,a,b} x[n, ci, oh2+po-a, ow2+pq-b] * w[ci, co, 2a+1-po, 2b+1-pq]
//
// computed with a tiled fp32 implicit-GEMM kernel (shared-memory staged,
// register 4x4 micro-tiles), with (+conv_bias, -bias, tanh) fused epilogue.
// ---------------------------------------------------------------------------

#define TILE_M 64
#define TILE_N 64
#define TILE_K 32
#define NTHREADS 256

// wp[phase][k=(ci,a,b)][co]  with  k = ci*4 + a*2 + b
__global__ void rearrange_weight_kernel(const float* __restrict__ w,
                                        float* __restrict__ wp) {
    int idx = blockIdx.x * blockDim.x + threadIdx.x;   // 4*256*64 elements
    if (idx >= 4 * 256 * 64) return;
    int co = idx & 63;
    int k  = (idx >> 6) & 255;
    int p  = idx >> 14;
    int po = p >> 1, pq = p & 1;
    int ci = k >> 2;
    int a  = (k >> 1) & 1;
    int b  = k & 1;
    int kh = 2 * a + 1 - po;
    int kw = 2 * b + 1 - pq;
    wp[idx] = w[(long)ci * 1024 + co * 16 + kh * 4 + kw];
}

__global__ void deconv_fused_kernel(const float* __restrict__ x,
                                    const float* __restrict__ wp,
                                    const float* __restrict__ conv_bias,
                                    const float* __restrict__ sub_bias,
                                    float* __restrict__ y,
                                    int N, int H, int W, int Ho, int Wo,
                                    int has_cb) {
    __shared__ float As[TILE_K][TILE_M];
    __shared__ float Bs[TILE_K][TILE_N];
    __shared__ int soff[TILE_M][4];
    __shared__ long long sbase[TILE_M];
    __shared__ long long sorow[TILE_M];

    const int phase = blockIdx.z;
    const int po = phase >> 1, pq = phase & 1;
    const int Hp = (Ho - po + 1) >> 1;
    const int Wp2 = (Wo - pq + 1) >> 1;
    const long M = (long)N * Hp * Wp2;
    const long row0 = (long)blockIdx.x * TILE_M;
    if (row0 >= M) return;

    const int tid = threadIdx.x;
    const long xsc = (long)H * W;
    const long xsn = 64 * xsc;
    const long ytc = (long)Ho * Wo;
    const long ytn = 64 * ytc;

    if (tid < TILE_M) {
        long row = row0 + tid;
        if (row < M) {
            long hw = (long)Hp * Wp2;
            long n = row / hw;
            long rem = row - n * hw;
            int oh2 = (int)(rem / Wp2);
            int ow2 = (int)(rem - (long)oh2 * Wp2);
            sbase[tid] = (long long)n * xsn;
            #pragma unroll
            for (int ab = 0; ab < 4; ++ab) {
                int a = ab >> 1, b = ab & 1;
                int ih = oh2 + po - a;
                int iw = ow2 + pq - b;
                bool v = (ih >= 0 && ih < H && iw >= 0 && iw < W);
                soff[tid][ab] = v ? (ih * W + iw) : -1;
            }
            int oh = 2 * oh2 + po;
            int ow = 2 * ow2 + pq;
            sorow[tid] = (long long)n * ytn + (long)oh * Wo + ow;
        } else {
            sbase[tid] = 0;
            #pragma unroll
            for (int ab = 0; ab < 4; ++ab) soff[tid][ab] = -1;
            sorow[tid] = -1;
        }
    }
    __syncthreads();

    const int tx = tid & 15;   // M dimension (4 rows each)
    const int ty = tid >> 4;   // N dimension (4 cols each)
    const int m0 = tx * 4;
    const int c0 = ty * 4;

    float acc[4][4];
    #pragma unroll
    for (int r = 0; r < 4; ++r)
        #pragma unroll
        for (int c = 0; c < 4; ++c)
            acc[r][c] = 0.0f;

    const float* wphase = wp + (long)phase * (256 * 64);

    for (int kc = 0; kc < 256 / TILE_K; ++kc) {
        // stage A tile (gather from x)
        #pragma unroll
        for (int it = 0; it < (TILE_K * TILE_M) / NTHREADS; ++it) {
            int idx = tid + it * NTHREADS;
            int m  = idx & (TILE_M - 1);
            int kk = idx >> 6;                 // 0..31
            int kg = kc * TILE_K + kk;
            int ci = kg >> 2;
            int ab = kg & 3;
            int off = soff[m][ab];
            float v = 0.0f;
            if (off >= 0) v = __ldg(x + sbase[m] + (long)ci * xsc + off);
            As[kk][m] = v;
        }
        // stage B tile (rearranged weights, coalesced)
        #pragma unroll
        for (int it = 0; it < (TILE_K * TILE_N) / NTHREADS; ++it) {
            int idx = tid + it * NTHREADS;
            int co = idx & 63;
            int kk = idx >> 6;
            Bs[kk][co] = __ldg(wphase + (long)(kc * TILE_K + kk) * 64 + co);
        }
        __syncthreads();

        #pragma unroll
        for (int kk = 0; kk < TILE_K; ++kk) {
            float4 av = *reinterpret_cast<const float4*>(&As[kk][m0]);
            float4 bv = *reinterpret_cast<const float4*>(&Bs[kk][c0]);
            acc[0][0] += av.x * bv.x; acc[0][1] += av.x * bv.y;
            acc[0][2] += av.x * bv.z; acc[0][3] += av.x * bv.w;
            acc[1][0] += av.y * bv.x; acc[1][1] += av.y * bv.y;
            acc[1][2] += av.y * bv.z; acc[1][3] += av.y * bv.w;
            acc[2][0] += av.z * bv.x; acc[2][1] += av.z * bv.y;
            acc[2][2] += av.z * bv.z; acc[2][3] += av.z * bv.w;
            acc[3][0] += av.w * bv.x; acc[3][1] += av.w * bv.y;
            acc[3][2] += av.w * bv.z; acc[3][3] += av.w * bv.w;
        }
        __syncthreads();
    }

    // fused epilogue: + conv bias, - bias, tanh
    #pragma unroll
    for (int r = 0; r < 4; ++r) {
        int m = m0 + r;
        long long orow = sorow[m];
        if (orow < 0) continue;
        #pragma unroll
        for (int c = 0; c < 4; ++c) {
            int co = c0 + c;
            float v = acc[r][c];
            if (has_cb) v += __ldg(conv_bias + co);
            v -= __ldg(sub_bias + co);
            y[orow + (long long)co * ytc] = tanhf(v);
        }
    }
}

// Fallback fused elementwise: y = tanh(x - sub_bias) per channel
__global__ void fused_bias_tanh_kernel(const float* __restrict__ x,
                                       const float* __restrict__ sub_bias,
                                       float* __restrict__ y,
                                       long HW, long total, int CO) {
    long idx = (long)blockIdx.x * blockDim.x + threadIdx.x;
    if (idx >= total) return;
    int co = (int)((idx / HW) % CO);
    y[idx] = tanhf(x[idx] - __ldg(sub_bias + co));
}

torch::Tensor deconv_forward(torch::Tensor x, torch::Tensor weight,
                             c10::optional<torch::Tensor> conv_bias,
                             torch::Tensor sub_bias,
                             int64_t stride, int64_t padding,
                             int64_t output_padding) {
    TORCH_CHECK(x.is_cuda() && x.dtype() == torch::kFloat32, "x must be CUDA fp32");
    auto xc = x.contiguous();
    auto wc = weight.contiguous();
    auto bc = sub_bias.contiguous();

    const int N = xc.size(0);
    const int CI = xc.size(1);
    const int H = xc.size(2);
    const int W = xc.size(3);
    const int CO = wc.size(1);
    const int KH = wc.size(2);
    const int KW = wc.size(3);

    const long Ho = (long)(H - 1) * stride - 2 * padding + KH + output_padding;
    const long Wo = (long)(W - 1) * stride - 2 * padding + KW + output_padding;

    auto stream = at::cuda::getCurrentCUDAStream();

    torch::Tensor cb;
    const float* cb_ptr = nullptr;
    int has_cb = 0;
    if (conv_bias.has_value() && conv_bias->defined()) {
        cb = conv_bias->contiguous();
        cb_ptr = cb.data_ptr<float>();
        has_cb = 1;
    }

    auto y = torch::empty({N, CO, Ho, Wo}, xc.options());

    bool fast = (CI == 64) && (CO == 64) && (KH == 4) && (KW == 4) &&
                (stride == 2) && (padding == 1) && (output_padding == 1) &&
                (Ho <= INT_MAX) && (Wo <= INT_MAX);

    if (!fast) {
        // safe fallback: cuDNN/ATen conv (with its bias) + one fused kernel
        auto tmp = at::conv_transpose2d(xc, wc, conv_bias.has_value() ? cb : torch::Tensor(),
                                        {stride, stride}, {padding, padding},
                                        {output_padding, output_padding}, 1);
        long total = tmp.numel();
        long HW = Ho * Wo;
        int threads = 256;
        long blocks = (total + threads - 1) / threads;
        fused_bias_tanh_kernel<<<(unsigned)blocks, threads, 0, stream>>>(
            tmp.data_ptr<float>(), bc.data_ptr<float>(), y.data_ptr<float>(),
            HW, total, CO);
        return y;
    }

    // rearranged weights: 4 phases x 256 x 64
    auto wp = torch::empty({4 * 256 * 64}, xc.options());
    rearrange_weight_kernel<<<256, 256, 0, stream>>>(wc.data_ptr<float>(), wp.data_ptr<float>());

    long maxM = 0;
    for (int p = 0; p < 4; ++p) {
        int po = p >> 1, pq = p & 1;
        long Hp = (Ho - po + 1) >> 1;
        long Wp2 = (Wo - pq + 1) >> 1;
        long Mp = (long)N * Hp * Wp2;
        if (Mp > maxM) maxM = Mp;
    }
    long tiles = (maxM + TILE_M - 1) / TILE_M;

    dim3 grid((unsigned)tiles, 1, 4);
    deconv_fused_kernel<<<grid, NTHREADS, 0, stream>>>(
        xc.data_ptr<float>(), wp.data_ptr<float>(), cb_ptr, bc.data_ptr<float>(),
        y.data_ptr<float>(), N, H, W, (int)Ho, (int)Wo, has_cb);
    return y;
}
"""

cpp_source = """
torch::Tensor deconv_forward(torch::Tensor x, torch::Tensor weight,
                             c10::optional<torch::Tensor> conv_bias,
                             torch::Tensor sub_bias,
                             int64_t stride, int64_t padding,
                             int64_t output_padding);
"""

deconv_mod = load_inline(
    name="deconv_fused_fp32",
    cpp_sources=cpp_source,
    cuda_sources=cuda_source,
    functions=["deconv_forward"],
    verbose=False,
    extra_cuda_cflags=["-O3", "--use_fast_math"],
)


class ModelNew(nn.Module):
    """
    Optimized model: transposed convolution via a fused fp32 implicit-GEMM
    CUDA kernel with (+conv_bias, -bias, tanh) fused in the epilogue.
    Falls back to ATen conv_transpose2d + a fused elementwise kernel for
    non-standard configurations.
    """
    def __init__(self, in_channels, out_channels, kernel_size, bias_shape, stride=2, padding=1, output_padding=1):
        super(ModelNew, self).__init__()
        self.conv_transpose = nn.ConvTranspose2d(in_channels, out_channels, kernel_size,
                                                 stride=stride, padding=padding,
                                                 output_padding=output_padding)
        self.bias = nn.Parameter(torch.randn(bias_shape))
        self.stride = stride
        self.padding = padding
        self.output_padding = output_padding

    def forward(self, x):
        return deconv_mod.deconv_forward(
            x,
            self.conv_transpose.weight,
            self.conv_transpose.bias,
            self.bias.view(-1),
            self.stride,
            self.padding,
            self.output_padding,
        )
```

Write an improved complete ModelNew: fix any error, and if the solution is already correct, make it faster. Output the new code in one ```python codeblock. Just the model code, no other text, and NO testing code.


Precision rule: the timed forward must compute in FP32 (TF32 tensor-core math is allowed, as in the PyTorch reference). Do not cast inputs, weights or intermediate results to float16 or bfloat16 and do not call half-precision kernels in forward().
