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 operation with asymmetric input and kernel size.

    Args:
        in_channels (int): Number of channels in the input tensor.
        out_channels (int): Number of channels produced by the convolution.
        kernel_size (tuple): Tuple of integers representing the kernel size (height, width).
        stride (tuple, optional): Tuple of integers representing the stride of the convolution. Defaults to (1, 1).
        padding (tuple, optional): Tuple of integers representing the padding applied to the input. Defaults to (0, 0).
        output_padding (tuple, optional): Tuple of integers representing the additional size added to one side of the output shape. Defaults to (0, 0).
        dilation (tuple, optional): Tuple of integers representing the spacing between kernel elements. Defaults to (1, 1).
        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: tuple, stride: tuple = (1, 1), padding: tuple = (0, 0), output_padding: tuple = (0, 0), dilation: tuple = (1, 1), 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, dilation=dilation, 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_in, width_in).

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

# Test code
batch_size = 64
in_channels = 64
out_channels = 128
kernel_size = (3, 5)
height_in = 128
width_in = 256

def get_inputs():
    x = torch.rand(batch_size, in_channels, height_in, width_in)
    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
from torch.utils.cpp_extension import load_inline


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

std::vector<torch::Tensor> pack_transposed_conv(
    torch::Tensor x, torch::Tensor weight,
    int64_t pad_h, int64_t pad_w, int64_t groups);

torch::Tensor run_convolution(
    torch::Tensor x, torch::Tensor weight,
    std::vector<int64_t> dilation, int64_t groups,
    bool deterministic, bool allow_tf32);
"""

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

// Fuse spatial padding and NCHW -> NHWC conversion. Explicit padding
// allows the subsequent convolution to operate entirely on valid windows.
__global__ void pad_pack_nchw(
    const float* __restrict__ input,
    float* __restrict__ output,
    int channels, int height, int width,
    int padded_height, int padded_width,
    int pad_h, int pad_w)
{
    __shared__ float tile[64][65];

    const int tx = threadIdx.x;
    const int ty = threadIdx.y;
    const int c0 = blockIdx.y * 64;
    const int spatial = padded_height * padded_width;
    const int s0 = blockIdx.x * 64;
    const int64_t input_base =
        int64_t(blockIdx.z) * channels * height * width;
    const int64_t output_base =
        int64_t(blockIdx.z) * channels * spatial;

    const int s1 = s0 + tx;
    const int s2 = s1 + 32;
    const int y1 = s1 / padded_width - pad_h;
    const int x1 = s1 % padded_width - pad_w;
    const int y2 = s2 / padded_width - pad_h;
    const int x2 = s2 % padded_width - pad_w;

    const bool valid1 =
        s1 < spatial &&
        static_cast<unsigned>(y1) < static_cast<unsigned>(height) &&
        static_cast<unsigned>(x1) < static_cast<unsigned>(width);
    const bool valid2 =
        s2 < spatial &&
        static_cast<unsigned>(y2) < static_cast<unsigned>(height) &&
        static_cast<unsigned>(x2) < static_cast<unsigned>(width);

    #pragma unroll
    for (int j = 0; j < 64; j += 8) {
        const int c = c0 + ty + j;
        float a = 0.0f;
        float b = 0.0f;
        if (c < channels) {
            const int64_t base =
                input_base + int64_t(c) * height * width;
            if (valid1)
                a = input[base + int64_t(y1) * width + x1];
            if (valid2)
                b = input[base + int64_t(y2) * width + x2];
        }
        tile[ty + j][tx] = a;
        tile[ty + j][tx + 32] = b;
    }
    __syncthreads();

    #pragma unroll
    for (int j = 0; j < 64; j += 8) {
        const int s = s0 + ty + j;
        if (s < spatial) {
            const int64_t base = output_base + int64_t(s) * channels;
            if (c0 + tx < channels)
                output[base + c0 + tx] = tile[tx][ty + j];
            if (c0 + tx + 32 < channels)
                output[base + c0 + tx + 32] = tile[tx + 32][ty + j];
        }
    }
}

__global__ void pad_pack_nhwc(
    const float* __restrict__ input,
    float* __restrict__ output,
    int channels, int height, int width,
    int padded_height, int padded_width,
    int pad_h, int pad_w, int64_t count)
{
    for (int64_t index =
             int64_t(blockIdx.x) * blockDim.x + threadIdx.x;
         index < count;
         index += int64_t(blockDim.x) * gridDim.x) {
        int64_t q = index;
        const int c = q % channels;
        q /= channels;
        const int x = q % padded_width - pad_w;
        q /= padded_width;
        const int y = q % padded_height - pad_h;
        const int64_t n = q / padded_height;

        float value = 0.0f;
        if (static_cast<unsigned>(y) < static_cast<unsigned>(height) &&
            static_cast<unsigned>(x) < static_cast<unsigned>(width)) {
            value = input[
                ((n * height + y) * width + x) * channels + c];
        }
        output[index] = value;
    }
}

__global__ void pack_weight_kernel(
    const float* __restrict__ input,
    float* __restrict__ output,
    int ci_per_group, int co_per_group,
    int kh, int kw, int64_t count)
{
    const int64_t index =
        int64_t(blockIdx.x) * blockDim.x + threadIdx.x;
    if (index >= count)
        return;

    int64_t q = index;
    const int ic = q % ci_per_group;
    q /= ci_per_group;
    const int s = q % kw;
    q /= kw;
    const int r = q % kh;
    const int oc = q / kh;

    const int group = oc / co_per_group;
    const int local_oc = oc % co_per_group;
    const int global_ic = group * ci_per_group + ic;

    const int64_t source =
        ((int64_t(global_ic) * co_per_group + local_oc) * kh
         + kh - 1 - r) * kw + kw - 1 - s;
    output[index] = input[source];
}

std::vector<torch::Tensor> pack_transposed_conv(
    torch::Tensor x, torch::Tensor weight,
    int64_t pad_h, int64_t pad_w, int64_t groups)
{
    TORCH_CHECK(x.is_cuda() && weight.is_cuda(), "Expected CUDA tensors");
    TORCH_CHECK(x.device() == weight.device(), "Device mismatch");
    TORCH_CHECK(x.scalar_type() == at::kFloat &&
                weight.scalar_type() == at::kFloat,
                "Expected float32 tensors");
    TORCH_CHECK(x.dim() == 4 && weight.dim() == 4,
                "Expected four-dimensional tensors");
    TORCH_CHECK(weight.is_contiguous(), "Weight must be contiguous");
    TORCH_CHECK(groups > 0 && weight.size(0) % groups == 0,
                "Invalid groups");
    TORCH_CHECK(x.size(1) == weight.size(0), "Channel mismatch");
    TORCH_CHECK(pad_h >= 0 && pad_w >= 0, "Invalid padding");

    const c10::cuda::CUDAGuard guard(x.device());
    const auto stream = at::cuda::getCurrentCUDAStream();

    const int64_t n = x.size(0);
    const int64_t ci = x.size(1);
    const int64_t h = x.size(2);
    const int64_t w = x.size(3);
    const int64_t hp = h + 2 * pad_h;
    const int64_t wp = w + 2 * pad_w;
    const int64_t kh = weight.size(2);
    const int64_t kw = weight.size(3);
    const int64_t cig = ci / groups;
    const int64_t cog = weight.size(1);
    const int64_t co = cog * groups;

    const bool channels_last =
        x.is_contiguous(at::MemoryFormat::ChannelsLast);

    torch::Tensor packed_x;
    if (channels_last && pad_h == 0 && pad_w == 0) {
        packed_x = x;
    } else {
        packed_x = torch::empty({n, hp, wp, ci}, x.options())
                       .permute({0, 3, 1, 2});

        if (channels_last) {
            const int64_t count = packed_x.numel();
            const int blocks = static_cast<int>(
                std::min<int64_t>((count + 255) / 256, 65535));
            pad_pack_nhwc<<<blocks, 256, 0, stream>>>(
                x.data_ptr<float>(), packed_x.data_ptr<float>(),
                ci, h, w, hp, wp, pad_h, pad_w, count);
        } else {
            TORCH_CHECK(x.is_contiguous(), "Input must be contiguous");
            const dim3 block(32, 8);
            const dim3 grid((hp * wp + 63) / 64, (ci + 63) / 64, n);
            pad_pack_nchw<<<grid, block, 0, stream>>>(
                x.data_ptr<float>(), packed_x.data_ptr<float>(),
                ci, h, w, hp, wp, pad_h, pad_w);
        }
        C10_CUDA_KERNEL_LAUNCH_CHECK();
    }

    auto packed_weight =
        torch::empty({co, kh, kw, cig}, weight.options())
            .permute({0, 3, 1, 2});

    const int64_t count = weight.numel();
    pack_weight_kernel<<<(count + 255) / 256, 256, 0, stream>>>(
        weight.data_ptr<float>(), packed_weight.data_ptr<float>(),
        cig, cog, kh, kw, count);
    C10_CUDA_KERNEL_LAUNCH_CHECK();

    return {packed_x, packed_weight};
}

torch::Tensor run_convolution(
    torch::Tensor x, torch::Tensor weight,
    std::vector<int64_t> dilation, int64_t groups,
    bool deterministic, bool allow_tf32)
{
    const c10::cuda::CUDAGuard guard(x.device());
    const std::vector<int64_t> padding{0, 0};
    const std::vector<int64_t> stride{1, 1};
    return at::cudnn_convolution(
        x, weight, padding, stride, dilation, groups,
        true, deterministic, allow_tf32);
}
"""

_extension = load_inline(
    name="asymmetric_transpose_conv_fused_padding_v3",
    cpp_sources=_cpp_source,
    cuda_sources=_cuda_source,
    functions=["pack_transposed_conv", "run_convolution"],
    extra_cflags=["-O3"],
    extra_cuda_cflags=["-O3"],
    verbose=False,
)


class _TransposedConvFunction(torch.autograd.Function):
    @staticmethod
    def forward(
        ctx, x, weight, bias, padding, dilation, groups,
        deterministic, allow_tf32
    ):
        packed_x, packed_weight = _extension.pack_transposed_conv(
            x, weight.contiguous(), padding[0], padding[1], groups
        )
        output = _extension.run_convolution(
            packed_x, packed_weight, list(dilation), groups,
            deterministic, allow_tf32
        )
        if bias is not None:
            output.add_(bias.reshape(1, -1, 1, 1))

        ctx.padding = padding
        ctx.dilation = dilation
        ctx.groups = groups
        ctx.input_shape = tuple(x.shape)
        ctx.padded_shape = tuple(packed_x.shape)
        ctx.weight_shape = tuple(packed_weight.shape)
        ctx.original_weight_shape = tuple(weight.shape)
        ctx.has_bias = bias is not None
        ctx.save_for_backward(
            packed_x if ctx.needs_input_grad[1] else None,
            packed_weight if ctx.needs_input_grad[0] else None,
        )
        return output

    @staticmethod
    def backward(ctx, grad_output):
        packed_x, packed_weight = ctx.saved_tensors
        grad_x = grad_weight = grad_bias = None

        if ctx.needs_input_grad[0]:
            padded_grad = torch.nn.grad.conv2d_input(
                ctx.padded_shape,
                packed_weight,
                grad_output,
                stride=1,
                padding=0,
                dilation=ctx.dilation,
                groups=ctx.groups,
            )
            ph, pw = ctx.padding
            h, w = ctx.input_shape[-2:]
            grad_x = padded_grad[:, :, ph:ph + h, pw:pw + w]

        if ctx.needs_input_grad[1]:
            packed_grad = torch.nn.grad.conv2d_weight(
                packed_x,
                ctx.weight_shape,
                grad_output,
                stride=1,
                padding=0,
                dilation=ctx.dilation,
                groups=ctx.groups,
            )
            ci, cog, kh, kw = ctx.original_weight_shape
            grad_weight = (
                packed_grad.reshape(
                    ctx.groups, cog, ci // ctx.groups, kh, kw
                )
                .transpose(1, 2)
                .flip((-2, -1))
                .reshape(ctx.original_weight_shape)
            )

        if ctx.has_bias and ctx.needs_input_grad[2]:
            grad_bias = grad_output.sum(dim=(0, 2, 3))

        return (
            grad_x, grad_weight, grad_bias,
            None, None, None, None, None
        )


class ModelNew(nn.Module):
    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        kernel_size: tuple,
        stride: tuple = (1, 1),
        padding: tuple = (0, 0),
        output_padding: tuple = (0, 0),
        dilation: tuple = (1, 1),
        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,
            dilation=dilation,
            groups=groups,
            bias=bias,
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        conv = self.conv_transpose2d
        padding = tuple(
            d * (k - 1) - p
            for d, k, p in zip(
                conv.dilation, conv.kernel_size, conv.padding
            )
        )

        eligible = (
            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 conv.stride == (1, 1)
            and conv.output_padding == (0, 0)
            and min(padding) >= 0
            and 0 < x.shape[0] <= 65535
            and 0 < x.shape[1] <= 65535 * 64
            and x.shape[2] > 0
            and x.shape[3] > 0
            and (
                (x.shape[2] + 2 * padding[0])
                * (x.shape[3] + 2 * padding[1])
                < 2**31 - 64
            )
            and torch.backends.cudnn.is_available()
            and torch.backends.cudnn.enabled
            and not torch.is_autocast_enabled()
        )
        if not eligible:
            return conv(x)

        if not (
            x.is_contiguous()
            or x.is_contiguous(memory_format=torch.channels_last)
        ):
            x = x.contiguous()

        return _TransposedConvFunction.apply(
            x,
            conv.weight,
            conv.bias,
            padding,
            conv.dilation,
            conv.groups,
            torch.backends.cudnn.deterministic
            or torch.are_deterministic_algorithms_enabled(),
            torch.backends.cudnn.allow_tf32,
        )
```
Result of evaluating it:
The solution is correct on the test inputs. Mean runtime 6.09 ms; the PyTorch reference (with TF32 enabled) takes 6.69 ms; speedup 1.099x.
Profiler, top device kernels of one forward pass of your solution:
| device kernel | total device time (us) | calls |
|---|---|---|
| sm80_xmma_fprop_implicit_gemm_tf32f32_tf32f32_f32_nhwckrsc_nhwc_tilesize128x128x16_stage4_ | 4193.7 | 1 |
| void cudnn::engines_precompiled::convertTensor_kernel<float, float, float, (cudnnKernelDat | 937.1 | 2 |
| pad_pack_nchw(float const*, float*, int, int, int, int, int, int, int) | 825.6 | 1 |
| pack_weight_kernel(float const*, float*, int, int, int, int, long) | 5.4 | 1 |

The fastest correct solution so far (1.117x 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>
#include <vector>

std::vector<torch::Tensor> pack_transposed_conv(
    torch::Tensor x, torch::Tensor weight, int64_t groups);

torch::Tensor run_convolution(
    torch::Tensor x,
    torch::Tensor weight,
    std::vector<int64_t> padding,
    std::vector<int64_t> dilation,
    int64_t groups,
    bool deterministic,
    bool allow_tf32);
"""

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

// A larger tile reduces block scheduling overhead on large feature maps.
__global__ void pack_input_kernel(
    const float* __restrict__ input,
    float* __restrict__ output,
    int channels,
    int64_t spatial)
{
    __shared__ float tile[64][65];

    const int tx = threadIdx.x;
    const int ty = threadIdx.y;
    const int c0 = int(blockIdx.y) * 64;
    const int64_t s0 = int64_t(blockIdx.x) * 64;
    const int64_t base = int64_t(blockIdx.z) * channels * spatial;

    #pragma unroll
    for (int j = 0; j < 64; j += 8) {
        const int c = c0 + ty + j;
        if (c < channels) {
            if (s0 + tx < spatial)
                tile[ty + j][tx] =
                    input[base + int64_t(c) * spatial + s0 + tx];
            if (s0 + tx + 32 < spatial)
                tile[ty + j][tx + 32] =
                    input[base + int64_t(c) * spatial + s0 + tx + 32];
        }
    }
    __syncthreads();

    #pragma unroll
    for (int j = 0; j < 64; j += 8) {
        const int64_t s = s0 + ty + j;
        if (s < spatial) {
            if (c0 + tx < channels)
                output[base + s * channels + c0 + tx] =
                    tile[tx][ty + j];
            if (c0 + tx + 32 < channels)
                output[base + s * channels + c0 + tx + 32] =
                    tile[tx + 32][ty + j];
        }
    }
}

__global__ void pack_weight_kernel(
    const float* __restrict__ input,
    float* __restrict__ output,
    int ci_per_group,
    int co_per_group,
    int kh,
    int kw,
    int64_t count)
{
    const int64_t index =
        int64_t(blockIdx.x) * blockDim.x + threadIdx.x;
    if (index >= count)
        return;

    int64_t q = index;
    const int ic = q % ci_per_group;
    q /= ci_per_group;
    const int s = q % kw;
    q /= kw;
    const int r = q % kh;
    const int oc = q / kh;

    const int group = oc / co_per_group;
    const int local_oc = oc % co_per_group;
    const int global_ic = group * ci_per_group + ic;

    const int64_t source =
        ((int64_t(global_ic) * co_per_group + local_oc) * kh
         + kh - 1 - r) * kw + kw - 1 - s;

    output[index] = input[source];
}

std::vector<torch::Tensor> pack_transposed_conv(
    torch::Tensor x, torch::Tensor weight, int64_t groups)
{
    TORCH_CHECK(x.is_cuda() && weight.is_cuda(),
                "Expected CUDA tensors");
    TORCH_CHECK(x.device() == weight.device(),
                "Input and weight must share a device");
    TORCH_CHECK(x.scalar_type() == at::kFloat &&
                weight.scalar_type() == at::kFloat,
                "Expected float32 tensors");
    TORCH_CHECK(x.dim() == 4 && weight.dim() == 4,
                "Expected four-dimensional tensors");
    TORCH_CHECK(weight.is_contiguous(),
                "Weight must be contiguous");
    TORCH_CHECK(groups > 0 && weight.size(0) % groups == 0,
                "Invalid groups");
    TORCH_CHECK(x.size(1) == weight.size(0),
                "Input channel count does not match weight");

    const c10::cuda::CUDAGuard guard(x.device());
    const auto stream = at::cuda::getCurrentCUDAStream();

    const int64_t n = x.size(0);
    const int64_t ci = x.size(1);
    const int64_t h = x.size(2);
    const int64_t w = x.size(3);
    const int64_t kh = weight.size(2);
    const int64_t kw = weight.size(3);
    const int64_t cig = ci / groups;
    const int64_t cog = weight.size(1);
    const int64_t co = cog * groups;

    torch::Tensor packed_x;
    if (x.is_contiguous(at::MemoryFormat::ChannelsLast)) {
        packed_x = x;
    } else {
        TORCH_CHECK(x.is_contiguous(), "Input must be contiguous");
        packed_x = torch::empty({n, h, w, ci}, x.options())
                       .permute({0, 3, 1, 2});

        if (x.numel() != 0) {
            const dim3 block(32, 8);
            const dim3 grid((h * w + 63) / 64, (ci + 63) / 64, n);
            pack_input_kernel<<<grid, block, 0, stream>>>(
                x.data_ptr<float>(),
                packed_x.data_ptr<float>(),
                static_cast<int>(ci),
                h * w);
            C10_CUDA_KERNEL_LAUNCH_CHECK();
        }
    }

    auto packed_weight =
        torch::empty({co, kh, kw, cig}, weight.options())
            .permute({0, 3, 1, 2});

    const int64_t count = weight.numel();
    if (count != 0) {
        pack_weight_kernel<<<(count + 255) / 256, 256, 0, stream>>>(
            weight.data_ptr<float>(),
            packed_weight.data_ptr<float>(),
            static_cast<int>(cig),
            static_cast<int>(cog),
            static_cast<int>(kh),
            static_cast<int>(kw),
            count);
        C10_CUDA_KERNEL_LAUNCH_CHECK();
    }

    return {packed_x, packed_weight};
}

torch::Tensor run_convolution(
    torch::Tensor x,
    torch::Tensor weight,
    std::vector<int64_t> padding,
    std::vector<int64_t> dilation,
    int64_t groups,
    bool deterministic,
    bool allow_tf32)
{
    const c10::cuda::CUDAGuard guard(x.device());
    const std::vector<int64_t> stride{1, 1};

    // cuDNN caches the selected algorithm for subsequent calls.
    return at::cudnn_convolution(
        x, weight, padding, stride, dilation, groups,
        true, deterministic, allow_tf32);
}
"""

_extension = load_inline(
    name="asymmetric_transpose_conv_tuned_v2",
    cpp_sources=_cpp_source,
    cuda_sources=_cuda_source,
    functions=["pack_transposed_conv", "run_convolution"],
    extra_cflags=["-O3"],
    extra_cuda_cflags=["-O3"],
    verbose=False,
)


class _TransposedConvFunction(torch.autograd.Function):
    @staticmethod
    def forward(
        ctx, x, weight, bias, padding, dilation, groups,
        deterministic, allow_tf32
    ):
        packed_x, packed_weight = _extension.pack_transposed_conv(
            x, weight.contiguous(), groups
        )
        output = _extension.run_convolution(
            packed_x, packed_weight, list(padding), list(dilation),
            groups, deterministic, allow_tf32
        )
        if bias is not None:
            output.add_(bias.reshape(1, -1, 1, 1))

        ctx.padding = padding
        ctx.dilation = dilation
        ctx.groups = groups
        ctx.input_shape = tuple(x.shape)
        ctx.weight_shape = tuple(packed_weight.shape)
        ctx.original_weight_shape = tuple(weight.shape)
        ctx.has_bias = bias is not None

        saved_x = packed_x if ctx.needs_input_grad[1] else None
        saved_weight = packed_weight if ctx.needs_input_grad[0] else None
        ctx.save_for_backward(saved_x, saved_weight)
        return output

    @staticmethod
    def backward(ctx, grad_output):
        packed_x, packed_weight = ctx.saved_tensors
        grad_x = grad_weight = grad_bias = None

        if ctx.needs_input_grad[0]:
            grad_x = torch.nn.grad.conv2d_input(
                ctx.input_shape,
                packed_weight,
                grad_output,
                stride=1,
                padding=ctx.padding,
                dilation=ctx.dilation,
                groups=ctx.groups,
            )

        if ctx.needs_input_grad[1]:
            packed_grad = torch.nn.grad.conv2d_weight(
                packed_x,
                ctx.weight_shape,
                grad_output,
                stride=1,
                padding=ctx.padding,
                dilation=ctx.dilation,
                groups=ctx.groups,
            )
            ci, cog, kh, kw = ctx.original_weight_shape
            grad_weight = (
                packed_grad.reshape(
                    ctx.groups, cog, ci // ctx.groups, kh, kw
                )
                .transpose(1, 2)
                .flip((-2, -1))
                .reshape(ctx.original_weight_shape)
            )

        if ctx.has_bias and ctx.needs_input_grad[2]:
            grad_bias = grad_output.sum(dim=(0, 2, 3))

        return (
            grad_x, grad_weight, grad_bias,
            None, None, None, None, None
        )


class ModelNew(nn.Module):
    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        kernel_size: tuple,
        stride: tuple = (1, 1),
        padding: tuple = (0, 0),
        output_padding: tuple = (0, 0),
        dilation: tuple = (1, 1),
        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,
            dilation=dilation,
            groups=groups,
            bias=bias,
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        conv = self.conv_transpose2d
        padding = tuple(
            d * (k - 1) - p
            for d, k, p in zip(
                conv.dilation, conv.kernel_size, conv.padding
            )
        )

        eligible = (
            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 conv.stride == (1, 1)
            and conv.output_padding == (0, 0)
            and min(padding) >= 0
            and 0 < x.shape[0] <= 65535
            and x.shape[2] > 0
            and x.shape[3] > 0
            and (x.shape[1] + 63) // 64 <= 65535
            and torch.backends.cudnn.is_available()
            and torch.backends.cudnn.enabled
            and not torch.is_autocast_enabled()
        )
        if not eligible:
            return conv(x)

        if not (
            x.is_contiguous()
            or x.is_contiguous(memory_format=torch.channels_last)
        ):
            x = x.contiguous()

        return _TransposedConvFunction.apply(
            x,
            conv.weight,
            conv.bias,
            padding,
            conv.dilation,
            conv.groups,
            torch.backends.cudnn.deterministic
            or torch.are_deterministic_algorithms_enabled(),
            torch.backends.cudnn.allow_tf32,
        )
```

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.
