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):
    """
    Performs a transposed 2D convolution with square input and square kernel.

    Args:
        in_channels (int): Number of channels in the input tensor.
        out_channels (int): Number of channels produced by the convolution.
        kernel_size (int): Size of the square convolution kernel.
        stride (int, optional): Stride of the convolution. Defaults to 1.
        padding (int, optional): Padding applied to the input. Defaults to 0.
        output_padding (int, optional): Additional size added to one side of the output shape. Defaults to 0.
        groups (int, optional): Number of blocked connections from input channels to output channels. Defaults to 1.
        bias (bool, optional): If `True`, adds a learnable bias to the output. Defaults to `False`.
    """
    def __init__(self, in_channels: int, out_channels: int, kernel_size: int, stride: int = 1, padding: int = 0, output_padding: int = 0, groups: int = 1, bias: bool = False):
        super(Model, self).__init__()
        self.conv_transpose2d = nn.ConvTranspose2d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, output_padding=output_padding, groups=groups, bias=bias)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        Performs the transposed 2D convolution.

        Args:
            x (torch.Tensor): Input tensor of shape (batch_size, in_channels, height, width).

        Returns:
            torch.Tensor: Output tensor of shape (batch_size, out_channels, height_out, width_out).
        """
        return self.conv_transpose2d(x)

# Test code
batch_size = 8
in_channels = 64  # double channels for heavier compute
out_channels = 64
kernel_size = 3
# larger square input
height = 1024
width = 1024

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

def get_init_inputs():
    return [in_channels, out_channels, kernel_size]  # Provide in_channels, out_channels, kernel_size for initialization
    ```
    
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 4 of 6 for this task. Your previous solution (turn 3) was:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.cpp_extension import load_inline


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

torch::Tensor to_channels_last_cuda(torch::Tensor x);
torch::Tensor prepare_conv_weight_cuda(torch::Tensor weight);
"""

_cuda_source = r"""
#include <torch/extension.h>
#include <ATen/cuda/CUDAContext.h>
#include <c10/cuda/CUDAGuard.h>
#include <c10/cuda/CUDAException.h>
#include <cuda_runtime.h>

constexpr int TILE_C = 64;
constexpr int TILE_S = 128;
constexpr int THREADS = 256;

__global__ void to_channels_last_kernel(
    const float* __restrict__ input,
    float* __restrict__ output,
    int64_t channels,
    int64_t spatial,
    int64_t spatial_tiles,
    int64_t channel_tiles)
{
    __shared__ float tile[TILE_C][TILE_S + 1];

    const int64_t spatial_tile = blockIdx.x % spatial_tiles;
    const int64_t channel_batch = blockIdx.x / spatial_tiles;
    const int64_t channel_tile = channel_batch % channel_tiles;
    const int64_t batch = channel_batch / channel_tiles;

    const int64_t channel_start = channel_tile * TILE_C;
    const int64_t spatial_start = spatial_tile * TILE_S;
    const int64_t batch_offset = batch * channels * spatial;

    #pragma unroll
    for (int index = threadIdx.x;
         index < TILE_C * TILE_S;
         index += THREADS) {
        const int c = index / TILE_S;
        const int s = index % TILE_S;
        const int64_t channel = channel_start + c;
        const int64_t position = spatial_start + s;

        float value = 0.0f;
        if (channel < channels && position < spatial) {
            value = input[batch_offset + channel * spatial + position];
        }
        tile[c][s] = value;
    }

    __syncthreads();

    #pragma unroll
    for (int index = threadIdx.x;
         index < TILE_C * TILE_S;
         index += THREADS) {
        const int s = index / TILE_C;
        const int c = index % TILE_C;
        const int64_t channel = channel_start + c;
        const int64_t position = spatial_start + s;

        if (channel < channels && position < spatial) {
            output[batch_offset + position * channels + channel] =
                tile[c][s];
        }
    }
}

// Convert [Cin, Cout, 3, 3] transposed-convolution weights into
// spatially reversed [Cout, Cin, 3, 3] channels-last convolution weights.
__global__ void prepare_conv_weight_kernel(
    const float* __restrict__ weight,
    float* __restrict__ output,
    int64_t in_channels,
    int64_t out_channels,
    int64_t count)
{
    const int64_t index =
        int64_t(blockIdx.x) * blockDim.x + threadIdx.x;

    if (index < count) {
        const int64_t ci = index % in_channels;
        const int64_t tap = (index / in_channels) % 9;
        const int64_t co = index / (in_channels * 9);

        output[index] =
            weight[(ci * out_channels + co) * 9 + (8 - tap)];
    }
}

torch::Tensor to_channels_last_cuda(torch::Tensor x)
{
    TORCH_CHECK(x.is_cuda() && x.scalar_type() == at::kFloat,
                "Expected a CUDA float32 tensor");
    TORCH_CHECK(x.dim() == 4 && x.is_contiguous(),
                "Expected contiguous NCHW input");

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

    auto output = torch::empty(
        x.sizes(),
        x.options().memory_format(at::MemoryFormat::ChannelsLast));

    if (x.numel() == 0) {
        return output;
    }

    const int64_t channels = x.size(1);
    const int64_t spatial = x.size(2) * x.size(3);
    const int64_t spatial_tiles = (spatial + TILE_S - 1) / TILE_S;
    const int64_t channel_tiles = (channels + TILE_C - 1) / TILE_C;
    const int64_t blocks = x.size(0) * spatial_tiles * channel_tiles;

    TORCH_CHECK(blocks <= 2147483647LL, "Input is too large");

    cudaStream_t stream = at::cuda::getCurrentCUDAStream();
    to_channels_last_kernel<<<
        static_cast<unsigned int>(blocks), THREADS, 0, stream>>>(
            x.data_ptr<float>(),
            output.data_ptr<float>(),
            channels,
            spatial,
            spatial_tiles,
            channel_tiles);
    C10_CUDA_KERNEL_LAUNCH_CHECK();

    return output;
}

torch::Tensor prepare_conv_weight_cuda(torch::Tensor weight)
{
    TORCH_CHECK(weight.is_cuda() && weight.scalar_type() == at::kFloat,
                "Expected CUDA float32 weights");
    TORCH_CHECK(
        weight.dim() == 4 && weight.is_contiguous() &&
        weight.size(2) == 3 && weight.size(3) == 3,
        "Expected contiguous [Cin, Cout, 3, 3] weights");

    c10::cuda::CUDAGuard guard(weight.device());

    const int64_t in_channels = weight.size(0);
    const int64_t out_channels = weight.size(1);

    auto output = torch::empty(
        {out_channels, in_channels, 3, 3},
        weight.options().memory_format(at::MemoryFormat::ChannelsLast));

    const int64_t count = weight.numel();
    if (count == 0) {
        return output;
    }

    cudaStream_t stream = at::cuda::getCurrentCUDAStream();
    prepare_conv_weight_kernel<<<
        static_cast<unsigned int>((count + 255) / 256),
        256, 0, stream>>>(
            weight.data_ptr<float>(),
            output.data_ptr<float>(),
            in_channels,
            out_channels,
            count);
    C10_CUDA_KERNEL_LAUNCH_CHECK();

    return output;
}
"""

_extension = load_inline(
    name="transpose_conv_as_forward_conv_v3",
    cpp_sources=_cpp_source,
    cuda_sources=_cuda_source,
    functions=["to_channels_last_cuda", "prepare_conv_weight_cuda"],
    extra_cflags=["-O3"],
    extra_cuda_cflags=["-O3"],
    verbose=False,
)


class _ChannelsLast(torch.autograd.Function):
    @staticmethod
    def forward(ctx, x):
        return _extension.to_channels_last_cuda(x)

    @staticmethod
    def backward(ctx, grad_output):
        return grad_output


class _PrepareConvWeight(torch.autograd.Function):
    @staticmethod
    def forward(ctx, weight):
        return _extension.prepare_conv_weight_cuda(weight)

    @staticmethod
    def backward(ctx, grad_output):
        return grad_output.flip((2, 3)).transpose(0, 1).contiguous()


class ModelNew(nn.Module):
    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        kernel_size: int,
        stride: int = 1,
        padding: int = 0,
        output_padding: int = 0,
        groups: int = 1,
        bias: bool = False,
    ):
        super().__init__()
        self.conv_transpose2d = nn.ConvTranspose2d(
            in_channels,
            out_channels,
            kernel_size,
            stride=stride,
            padding=padding,
            output_padding=output_padding,
            groups=groups,
            bias=bias,
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        conv = self.conv_transpose2d

        if not (
            x.is_cuda
            and x.ndim == 4
            and x.dtype == torch.float32
            and conv.weight.dtype == torch.float32
            and x.device == conv.weight.device
            and x.shape[1] == conv.in_channels
            and x.shape[2] > 0
            and x.shape[3] > 0
            and conv.weight.is_contiguous()
            and conv.kernel_size == (3, 3)
            and conv.stride == (1, 1)
            and conv.padding == (0, 0)
            and conv.output_padding == (0, 0)
            and conv.dilation == (1, 1)
            and conv.groups == 1
            and not torch.is_autocast_enabled()
        ):
            return conv(x)

        if not x.is_contiguous(memory_format=torch.channels_last):
            if x.is_contiguous():
                x = _ChannelsLast.apply(x)
            else:
                x = x.contiguous(memory_format=torch.channels_last)

        weight = _PrepareConvWeight.apply(conv.weight)

        # A stride-one transposed convolution is a forward convolution
        # with exchanged channel axes, reversed spatial weights, and
        # padding equal to kernel_size - 1.
        return F.conv2d(
            x,
            weight,
            conv.bias,
            stride=1,
            padding=2,
            dilation=1,
            groups=1,
        )
```
Result of evaluating it:
The solution is correct on the test inputs. Mean runtime 13.7 ms; the PyTorch reference (with TF32 enabled) takes 13.5 ms; speedup 0.985x.
Profiler, top device kernels of one forward pass of your solution:
| device kernel | total device time (us) | calls |
|---|---|---|
| void cutlass__5x_cudnn::Kernel<cutlass_tensorop_s1688fprop_optimized_tf32_128x64_32x3_nhwc | 6771.4 | 1 |
| void cudnn::engines_precompiled::convertTensor_kernel<float, float, float, (cudnnKernelDat | 3565.8 | 2 |
| to_channels_last_kernel(float const*, float*, long, long, long, long) | 3262.5 | 1 |
| prepare_conv_weight_kernel(float const*, float*, long, long, long) | 4.0 | 1 |

The fastest correct solution so far (1.000x speedup) is:
```python
import torch
import torch.nn as nn
from torch.utils.cpp_extension import load_inline


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

torch::Tensor transposed_conv_cuda(
    torch::Tensor x,
    torch::Tensor weight,
    c10::optional<torch::Tensor> bias);
"""

_cuda_source = r"""
#include <torch/extension.h>
#include <ATen/cuda/CUDAContext.h>
#include <c10/cuda/CUDAGuard.h>
#include <c10/cuda/CUDAException.h>
#include <cuda_runtime.h>
#include <mma.h>

namespace wmma = nvcuda::wmma;

constexpr int CHANNELS = 64;
constexpr int REDUCTION = 64 * 3 * 3;
constexpr int TILE_M = 32;
constexpr int TILE_N = 64;
constexpr int TILE_K = 32;

// Pack the original [input_channel, output_channel, kh, kw] weights
// into a matrix, retaining a TF32 high part and a TF32 residual.
__global__ void pack_weights(
    const float* __restrict__ weight,
    float* __restrict__ packed)
{
#if __CUDA_ARCH__ >= 800
    int index = blockIdx.x * blockDim.x + threadIdx.x;
    if (index < CHANNELS * REDUCTION) {
        int co = index / REDUCTION;
        int k = index % REDUCTION;
        int ci = k % CHANNELS;
        int tap = k / CHANNELS;
        float value = weight[(ci * CHANNELS + co) * 9 + tap];
        float high = wmma::__float_to_tf32(value);
        packed[index] = high;
        packed[CHANNELS * REDUCTION + index] =
            wmma::__float_to_tf32(value - high);
    }
#endif
}

__global__ void transposed_conv_kernel(
    const float* __restrict__ input,
    const float* __restrict__ weights,
    const float* __restrict__ bias,
    float* __restrict__ output,
    int height,
    int width)
{
#if __CUDA_ARCH__ >= 800
    __shared__ __align__(32) float a_high[TILE_M * TILE_K];
    __shared__ __align__(32) float a_low[TILE_M * TILE_K];
    __shared__ __align__(32) float b_high[TILE_K * TILE_N];
    __shared__ __align__(32) float b_low[TILE_K * TILE_N];
    __shared__ __align__(32) float result[TILE_M * TILE_N];

    const int tid = threadIdx.x;
    const int warp = tid / 32;
    const int warp_m = warp / 4;
    const int warp_n = warp % 4;

    const int channel_base = blockIdx.y * TILE_M;
    const int64_t position_base = int64_t(blockIdx.x) * TILE_N;
    const int batch = blockIdx.z;
    const int output_height = height + 2;
    const int output_width = width + 2;
    const int64_t input_area = int64_t(height) * width;
    const int64_t output_area =
        int64_t(output_height) * output_width;

    wmma::fragment<wmma::accumulator, 16, 16, 8, float> accumulator;
    wmma::fill_fragment(accumulator, 0.0f);

    for (int k_base = 0; k_base < REDUCTION; k_base += TILE_K) {
        for (int index = tid; index < TILE_M * TILE_K; index += 256) {
            int row = index / TILE_K;
            int k = index % TILE_K;
            int offset = (channel_base + row) * REDUCTION + k_base + k;
            a_high[index] = weights[offset];
            a_low[index] = weights[CHANNELS * REDUCTION + offset];
        }

        for (int index = tid; index < TILE_K * TILE_N; index += 256) {
            int k = k_base + index / TILE_N;
            int64_t position = position_base + index % TILE_N;
            int ci = k % CHANNELS;
            int tap = k / CHANNELS;

            float value = 0.0f;
            if (position < output_area) {
                int oy = int(position / output_width);
                int ox = int(position % output_width);
                int iy = oy - tap / 3;
                int ix = ox - tap % 3;

                if (iy >= 0 && iy < height && ix >= 0 && ix < width) {
                    int64_t offset =
                        (int64_t(batch) * CHANNELS + ci) * input_area
                        + int64_t(iy) * width + ix;
                    value = input[offset];
                }
            }

            float high = wmma::__float_to_tf32(value);
            b_high[index] = high;
            b_low[index] = wmma::__float_to_tf32(value - high);
        }
        __syncthreads();

        #pragma unroll
        for (int step = 0; step < TILE_K; step += 8) {
            wmma::fragment<
                wmma::matrix_a, 16, 16, 8,
                wmma::precision::tf32, wmma::row_major> ah, al;
            wmma::fragment<
                wmma::matrix_b, 16, 16, 8,
                wmma::precision::tf32, wmma::row_major> bh, bl;

            int a_offset = warp_m * 16 * TILE_K + step;
            int b_offset = step * TILE_N + warp_n * 16;

            wmma::load_matrix_sync(ah, a_high + a_offset, TILE_K);
            wmma::load_matrix_sync(al, a_low + a_offset, TILE_K);
            wmma::load_matrix_sync(bh, b_high + b_offset, TILE_N);
            wmma::load_matrix_sync(bl, b_low + b_offset, TILE_N);

            // Residual products recover substantially more precision
            // than a single TF32 matrix multiplication.
            wmma::mma_sync(accumulator, al, bh, accumulator);
            wmma::mma_sync(accumulator, ah, bl, accumulator);
            wmma::mma_sync(accumulator, ah, bh, accumulator);
        }
        __syncthreads();
    }

    wmma::store_matrix_sync(
        result + warp_m * 16 * TILE_N + warp_n * 16,
        accumulator,
        TILE_N,
        wmma::mem_row_major);
    __syncthreads();

    for (int index = tid; index < TILE_M * TILE_N; index += 256) {
        int co = channel_base + index / TILE_N;
        int64_t position = position_base + index % TILE_N;
        if (position < output_area) {
            float value = result[index];
            if (bias != nullptr) {
                value += bias[co];
            }
            output[(int64_t(batch) * CHANNELS + co) * output_area
                   + position] = value;
        }
    }
#endif
}

torch::Tensor transposed_conv_cuda(
    torch::Tensor x,
    torch::Tensor weight,
    c10::optional<torch::Tensor> bias)
{
    TORCH_CHECK(x.is_cuda() && weight.is_cuda(), "Expected CUDA tensors");
    TORCH_CHECK(
        x.scalar_type() == at::kFloat &&
        weight.scalar_type() == at::kFloat,
        "Expected float32 tensors");
    TORCH_CHECK(x.is_contiguous() && weight.is_contiguous(),
                "Expected contiguous tensors");
    TORCH_CHECK(x.dim() == 4 && x.size(1) == CHANNELS,
                "Expected NCHW input with 64 channels");
    TORCH_CHECK(
        weight.dim() == 4 &&
        weight.size(0) == CHANNELS &&
        weight.size(1) == CHANNELS &&
        weight.size(2) == 3 &&
        weight.size(3) == 3,
        "Expected weight shape [64, 64, 3, 3]");
    TORCH_CHECK(x.device() == weight.device(), "Device mismatch");

    c10::cuda::CUDAGuard guard(x.device());
    TORCH_CHECK(at::cuda::getCurrentDeviceProperties()->major >= 8,
                "This kernel requires compute capability 8.0 or newer");

    const float* bias_ptr = nullptr;
    if (bias.has_value()) {
        const auto& b = bias.value();
        TORCH_CHECK(
            b.device() == x.device() &&
            b.scalar_type() == at::kFloat &&
            b.is_contiguous() &&
            b.numel() == CHANNELS,
            "Invalid bias");
        bias_ptr = b.data_ptr<float>();
    }

    const int batch = int(x.size(0));
    const int height = int(x.size(2));
    const int width = int(x.size(3));
    auto output = torch::empty(
        {x.size(0), CHANNELS, x.size(2) + 2, x.size(3) + 2},
        x.options());

    if (batch == 0) {
        return output;
    }

    auto packed = torch::empty({2, CHANNELS, REDUCTION}, weight.options());
    cudaStream_t stream = c10::cuda::getCurrentCUDAStream();

    pack_weights<<<(CHANNELS * REDUCTION + 255) / 256, 256, 0, stream>>>(
        weight.data_ptr<float>(),
        packed.data_ptr<float>());
    C10_CUDA_KERNEL_LAUNCH_CHECK();

    int64_t output_area = int64_t(height + 2) * (width + 2);
    dim3 grid(
        unsigned((output_area + TILE_N - 1) / TILE_N),
        CHANNELS / TILE_M,
        unsigned(batch));

    transposed_conv_kernel<<<grid, 256, 0, stream>>>(
        x.data_ptr<float>(),
        packed.data_ptr<float>(),
        bias_ptr,
        output.data_ptr<float>(),
        height,
        width);
    C10_CUDA_KERNEL_LAUNCH_CHECK();

    return output;
}
"""

_extension = load_inline(
    name="custom_transposed_conv2d_tf32_residual_v1",
    cpp_sources=_cpp_source,
    cuda_sources=_cuda_source,
    functions=["transposed_conv_cuda"],
    extra_cflags=["-O3"],
    extra_cuda_cflags=["-O3"],
    verbose=False,
)


class ModelNew(nn.Module):
    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        kernel_size: int,
        stride: int = 1,
        padding: int = 0,
        output_padding: int = 0,
        groups: int = 1,
        bias: bool = False,
    ):
        super().__init__()
        self.conv_transpose2d = nn.ConvTranspose2d(
            in_channels,
            out_channels,
            kernel_size,
            stride=stride,
            padding=padding,
            output_padding=output_padding,
            groups=groups,
            bias=bias,
        )
        conv = self.conv_transpose2d
        self._specialized = (
            in_channels == 64
            and out_channels == 64
            and conv.kernel_size == (3, 3)
            and conv.stride == (1, 1)
            and conv.padding == (0, 0)
            and conv.output_padding == (0, 0)
            and groups == 1
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        conv = self.conv_transpose2d
        needs_grad = torch.is_grad_enabled() and (
            x.requires_grad
            or conv.weight.requires_grad
            or (conv.bias is not None and conv.bias.requires_grad)
        )
        if (
            self._specialized
            and not needs_grad
            and x.is_cuda
            and x.dtype == torch.float32
            and conv.weight.dtype == torch.float32
            and x.is_contiguous()
            and conv.weight.is_contiguous()
            and x.ndim == 4
            and x.shape[1] == 64
            and x.shape[0] <= 65535
            and x.shape[2] > 0
            and x.shape[3] > 0
            and not torch.is_autocast_enabled()
            and torch.cuda.get_device_capability(x.device)[0] >= 8
        ):
            return _extension.transposed_conv_cuda(x, conv.weight, conv.bias)
        return conv(x)
```

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.
