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 3D transposed convolution, scales the output, applies batch normalization, 
    and then performs global average pooling. 
    """
    def __init__(self, in_channels, out_channels, kernel_size, scale_factor, eps=1e-5, momentum=0.1):
        super(Model, self).__init__()
        self.conv_transpose = nn.ConvTranspose3d(in_channels, out_channels, kernel_size)
        self.scale_factor = scale_factor
        self.batch_norm = nn.BatchNorm3d(out_channels, eps=eps, momentum=momentum)
        self.global_avg_pool = nn.AdaptiveAvgPool3d((1, 1, 1))

    def forward(self, x):
        x = self.conv_transpose(x)
        x = x * self.scale_factor
        x = self.batch_norm(x)
        x = self.global_avg_pool(x)
        return x

batch_size = 16
in_channels = 64
out_channels = 128
depth, height, width = 16, 32, 32
kernel_size = 5
scale_factor = 2.0

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

def get_init_inputs():
    return [in_channels, out_channels, kernel_size, scale_factor]
    ```
    
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 6 of 6 for this task. Your previous solution (turn 5) was:
```python
import torch
import torch.nn as nn
from torch.utils.cpp_extension import load_inline


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

std::vector<torch::Tensor> pack_spectrum(torch::Tensor spectrum, double scale);

torch::Tensor tensorcore_bn_pool(
    torch::Tensor inputs,
    torch::Tensor weights,
    torch::Tensor input_dc,
    torch::Tensor weight_dc,
    torch::Tensor conv_bias,
    torch::Tensor gamma,
    torch::Tensor beta,
    torch::Tensor running_mean,
    torch::Tensor running_var,
    torch::Tensor batches_tracked,
    int64_t cin,
    int64_t channels,
    int64_t spatial,
    int64_t width,
    double scale,
    double eps,
    double momentum,
    bool cumulative);
"""

_CUDA = r"""
#include <torch/extension.h>
#include <ATen/cuda/CUDAContext.h>
#include <c10/cuda/CUDAGuard.h>
#include <c10/cuda/CUDAException.h>
#include <c10/util/complex.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <cuda_fp16.h>
#include <mma.h>
#include <cstdint>
#include <vector>

namespace wmma = nvcuda::wmma;

// Transpose the spectrum and split each component into two FP16 values.
// Keeping the residual avoids the accuracy loss of a single FP16 transform.
__global__ void pack_kernel(
    const float2* __restrict__ source,
    half* __restrict__ packed,
    float* __restrict__ dc,
    int rows,
    int frequencies,
    int channels,
    int height,
    int width_complex,
    int64_t s0,
    int64_t s1,
    int64_t s2,
    int64_t s3,
    int64_t s4,
    float scale)
{
    __shared__ float2 tile[32][33];

    const int tx = threadIdx.x;
    const int ty = threadIdx.y;
    const int source_row = blockIdx.y * 32 + ty;
    const int source_frequency = blockIdx.x * 32 + tx;

    #pragma unroll
    for (int j = 0; j < 32; j += 8) {
        const int row = source_row + j;
        float2 value = make_float2(0.0f, 0.0f);

        if (row < rows && source_frequency < frequencies) {
            const int w = source_frequency % width_complex;
            const int h = (source_frequency / width_complex) % height;
            const int d = source_frequency / (width_complex * height);
            const int64_t offset =
                int64_t(row / channels) * s0
                + int64_t(row % channels) * s1
                + int64_t(d) * s2
                + int64_t(h) * s3
                + int64_t(w) * s4;
            value = source[offset];
        }
        tile[ty + j][tx] = value;
    }
    __syncthreads();

    const int output_row = blockIdx.y * 32 + tx;
    const int64_t plane = int64_t(rows) * frequencies;

    #pragma unroll
    for (int j = 0; j < 32; j += 8) {
        const int frequency = blockIdx.x * 32 + ty + j;
        if (output_row < rows && frequency < frequencies) {
            const float2 value = tile[tx][ty + j];
            const float re = value.x * scale;
            const float im = value.y * scale;
            const half re_hi = __float2half_rn(re);
            const half im_hi = __float2half_rn(im);
            const half re_lo = __float2half_rn(re - __half2float(re_hi));
            const half im_lo = __float2half_rn(im - __half2float(im_hi));
            const int64_t offset = int64_t(frequency) * rows + output_row;

            packed[offset] = re_hi;
            packed[plane + offset] = im_hi;
            packed[2 * plane + offset] = re_lo;
            packed[3 * plane + offset] = im_lo;

            if (frequency == 0)
                dc[output_row] = value.x;
        }
    }
}

std::vector<torch::Tensor> pack_spectrum(
    torch::Tensor spectrum,
    double scale)
{
    const c10::cuda::CUDAGuard guard(spectrum.device());

    const int rows = spectrum.size(0) * spectrum.size(1);
    const int frequencies =
        spectrum.size(2) * spectrum.size(3) * spectrum.size(4);

    auto packed = torch::empty(
        {4, frequencies, rows}, spectrum.options().dtype(torch::kFloat16));
    auto dc = torch::empty(
        {rows}, spectrum.options().dtype(torch::kFloat32));

    pack_kernel<<<
        dim3((frequencies + 31) / 32, (rows + 31) / 32),
        dim3(32, 8), 0, at::cuda::getCurrentCUDAStream()>>>(
            reinterpret_cast<const float2*>(
                spectrum.data_ptr<c10::complex<float>>()),
            reinterpret_cast<half*>(packed.data_ptr<at::Half>()),
            dc.data_ptr<float>(),
            rows, frequencies, spectrum.size(1),
            spectrum.size(3), spectrum.size(4),
            spectrum.stride(0), spectrum.stride(1),
            spectrum.stride(2), spectrum.stride(3), spectrum.stride(4),
            float(scale));
    C10_CUDA_KERNEL_LAUNCH_CHECK();

    return {packed, dc};
}

constexpr int BATCH = 16;
constexpr int CHANNEL_TILE = 128;
constexpr int REDUCTION_CHUNK = 1024;

// Each warp computes all 16 samples for 16 output channels.
// The high-low cross terms retain approximately FP32 input precision.
__global__ void tensorcore_energy_kernel(
    const half* __restrict__ inputs,
    const half* __restrict__ weights,
    float* __restrict__ partial,
    int cin,
    int channels,
    int frequencies,
    int width)
{
    __shared__ float real_output[BATCH][CHANNEL_TILE];
    __shared__ float imag_output[BATCH][CHANNEL_TILE];

    const int frequency = blockIdx.x;
    const int warp = threadIdx.x >> 5;
    const int channel_start = blockIdx.y * CHANNEL_TILE;
    const int warp_channel = channel_start + warp * 16;

    using A = wmma::fragment<
        wmma::matrix_a, 16, 16, 16, half, wmma::row_major>;
    using B = wmma::fragment<
        wmma::matrix_b, 16, 16, 16, half, wmma::row_major>;
    using C = wmma::fragment<
        wmma::accumulator, 16, 16, 16, float>;

    C real_acc, imag_acc;
    wmma::fill_fragment(real_acc, 0.0f);
    wmma::fill_fragment(imag_acc, 0.0f);

    if (warp_channel < channels) {
        const int64_t input_plane = int64_t(frequencies) * BATCH * cin;
        const int64_t weight_plane = int64_t(frequencies) * cin * channels;

        const half* input_base = inputs + int64_t(frequency) * BATCH * cin;
        const half* weight_base =
            weights + int64_t(frequency) * cin * channels + warp_channel;

        for (int k = 0; k < cin; k += 16) {
            A ar, ai, ar_low, ai_low;
            B br, bi, br_low, bi_low;

            wmma::load_matrix_sync(ar, input_base + k, cin);
            wmma::load_matrix_sync(ai, input_base + input_plane + k, cin);
            wmma::load_matrix_sync(
                ar_low, input_base + 2 * input_plane + k, cin);
            wmma::load_matrix_sync(
                ai_low, input_base + 3 * input_plane + k, cin);

            const int64_t offset = int64_t(k) * channels;
            wmma::load_matrix_sync(br, weight_base + offset, channels);
            wmma::load_matrix_sync(
                bi, weight_base + weight_plane + offset, channels);
            wmma::load_matrix_sync(
                br_low, weight_base + 2 * weight_plane + offset, channels);
            wmma::load_matrix_sync(
                bi_low, weight_base + 3 * weight_plane + offset, channels);

            wmma::mma_sync(real_acc, ar, br, real_acc);
            wmma::mma_sync(real_acc, ar, br_low, real_acc);
            wmma::mma_sync(real_acc, ar_low, br, real_acc);

            wmma::mma_sync(imag_acc, ar, bi, imag_acc);
            wmma::mma_sync(imag_acc, ar, bi_low, imag_acc);
            wmma::mma_sync(imag_acc, ar_low, bi, imag_acc);
            wmma::mma_sync(imag_acc, ai, br, imag_acc);
            wmma::mma_sync(imag_acc, ai, br_low, imag_acc);
            wmma::mma_sync(imag_acc, ai_low, br, imag_acc);

            #pragma unroll
            for (int i = 0; i < ai.num_elements; ++i) {
                ai.x[i] = __hneg(ai.x[i]);
                ai_low.x[i] = __hneg(ai_low.x[i]);
            }

            wmma::mma_sync(real_acc, ai, bi, real_acc);
            wmma::mma_sync(real_acc, ai, bi_low, real_acc);
            wmma::mma_sync(real_acc, ai_low, bi, real_acc);
        }

        wmma::store_matrix_sync(
            &real_output[0][warp * 16],
            real_acc, CHANNEL_TILE, wmma::mem_row_major);
        wmma::store_matrix_sync(
            &imag_output[0][warp * 16],
            imag_acc, CHANNEL_TILE, wmma::mem_row_major);
    }
    __syncthreads();

    if (threadIdx.x < CHANNEL_TILE) {
        const int local_channel = threadIdx.x;
        const int channel = channel_start + local_channel;

        if (channel < channels) {
            float energy = 0.0f;
            #pragma unroll
            for (int n = 0; n < BATCH; ++n) {
                const float re = real_output[n][local_channel];
                const float im = imag_output[n][local_channel];
                energy = fmaf(re, re, energy);
                energy = fmaf(im, im, energy);
            }

            const int fw = frequency % (width / 2 + 1);
            const float multiplicity =
                (fw == 0 || (!(width & 1) && fw == width / 2))
                    ? 1.0f : 2.0f;

            partial[int64_t(frequency) * channels + channel] =
                energy * multiplicity;
        }
    }
}

__global__ void reduce_energy_kernel(
    const float* __restrict__ partial,
    double* __restrict__ reduced,
    int frequencies,
    int channels)
{
    __shared__ double sums[8][32];

    const int lane = threadIdx.x & 31;
    const int warp = threadIdx.x >> 5;
    const int channel = blockIdx.x * 32 + lane;
    const int start = blockIdx.y * REDUCTION_CHUNK;
    const int end = min(start + REDUCTION_CHUNK, frequencies);

    double sum = 0.0;
    if (channel < channels) {
        for (int f = start + warp; f < end; f += 8)
            sum += double(partial[int64_t(f) * channels + channel]);
    }

    sums[warp][lane] = sum;
    __syncthreads();

    if (warp == 0 && channel < channels) {
        double total = 0.0;
        #pragma unroll
        for (int w = 0; w < 8; ++w)
            total += sums[w][lane];
        reduced[int64_t(blockIdx.y) * channels + channel] = total;
    }
}

__global__ void finish_kernel(
    const float* __restrict__ input_dc,
    const float* __restrict__ weight_dc,
    const double* __restrict__ reduced,
    const float* __restrict__ conv_bias,
    const float* __restrict__ gamma,
    const float* __restrict__ beta,
    float* __restrict__ running_mean,
    float* __restrict__ running_var,
    int64_t* __restrict__ batches_tracked,
    float* __restrict__ output,
    int cin,
    int channels,
    int groups,
    int64_t spatial,
    float scale,
    float eps,
    float momentum,
    bool cumulative)
{
    __shared__ float update_factor;
    if (threadIdx.x == 0) {
        const int64_t count = ++batches_tracked[0];
        update_factor = cumulative ? 1.0f / float(count) : momentum;
    }
    __syncthreads();

    for (int channel = threadIdx.x;
         channel < channels;
         channel += blockDim.x) {
        double means[BATCH];
        double mean = 0.0;

        #pragma unroll
        for (int n = 0; n < BATCH; ++n) {
            double dc = 0.0;
            for (int k = 0; k < cin; ++k) {
                dc += double(input_dc[n * cin + k])
                    * double(weight_dc[int64_t(k) * channels + channel]);
            }
            means[n] = dc / double(spatial);
            mean += means[n];
        }
        mean /= double(BATCH);

        double energy = 0.0;
        for (int group = 0; group < groups; ++group)
            energy += reduced[int64_t(group) * channels + channel];

        // Packed inputs are scaled by 1/32 and weights by 256.
        const double second_moment =
            energy / (64.0 * double(BATCH)
                      * double(spatial) * double(spatial));
        const double variance = fmax(second_moment - mean * mean, 0.0);
        const double scaled_variance =
            variance * double(scale) * double(scale);
        const float inverse_std = rsqrtf(float(scaled_variance) + eps);

        const float scaled_mean =
            float((mean + double(conv_bias[channel])) * double(scale));
        const double count = double(BATCH) * double(spatial);
        const float unbiased_variance =
            float(scaled_variance * count / (count - 1.0));

        running_mean[channel] =
            (1.0f - update_factor) * running_mean[channel]
            + update_factor * scaled_mean;
        running_var[channel] =
            (1.0f - update_factor) * running_var[channel]
            + update_factor * unbiased_variance;

        #pragma unroll
        for (int n = 0; n < BATCH; ++n) {
            const float centered =
                float((means[n] - mean) * double(scale));
            output[n * channels + channel] =
                centered * inverse_std * gamma[channel] + beta[channel];
        }
    }
}

torch::Tensor tensorcore_bn_pool(
    torch::Tensor inputs,
    torch::Tensor weights,
    torch::Tensor input_dc,
    torch::Tensor weight_dc,
    torch::Tensor conv_bias,
    torch::Tensor gamma,
    torch::Tensor beta,
    torch::Tensor running_mean,
    torch::Tensor running_var,
    torch::Tensor batches_tracked,
    int64_t cin,
    int64_t channels,
    int64_t spatial,
    int64_t width,
    double scale,
    double eps,
    double momentum,
    bool cumulative)
{
    const c10::cuda::CUDAGuard guard(inputs.device());

    const int frequencies = inputs.size(1);
    const int groups =
        (frequencies + REDUCTION_CHUNK - 1) / REDUCTION_CHUNK;

    auto options = conv_bias.options();
    auto partial = torch::empty({frequencies, channels}, options);
    auto reduced = torch::empty(
        {groups, channels}, options.dtype(torch::kFloat64));
    auto output = torch::empty({BATCH, channels, 1, 1, 1}, options);

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

    tensorcore_energy_kernel<<<
        dim3(frequencies, (channels + CHANNEL_TILE - 1) / CHANNEL_TILE),
        256, 0, stream>>>(
            reinterpret_cast<const half*>(inputs.data_ptr<at::Half>()),
            reinterpret_cast<const half*>(weights.data_ptr<at::Half>()),
            partial.data_ptr<float>(),
            int(cin), int(channels), frequencies, int(width));
    C10_CUDA_KERNEL_LAUNCH_CHECK();

    reduce_energy_kernel<<<
        dim3((channels + 31) / 32, groups), 256, 0, stream>>>(
            partial.data_ptr<float>(),
            reduced.data_ptr<double>(),
            frequencies, int(channels));
    C10_CUDA_KERNEL_LAUNCH_CHECK();

    finish_kernel<<<1, 256, 0, stream>>>(
        input_dc.data_ptr<float>(),
        weight_dc.data_ptr<float>(),
        reduced.data_ptr<double>(),
        conv_bias.data_ptr<float>(),
        gamma.data_ptr<float>(),
        beta.data_ptr<float>(),
        running_mean.data_ptr<float>(),
        running_var.data_ptr<float>(),
        batches_tracked.data_ptr<int64_t>(),
        output.data_ptr<float>(),
        int(cin), int(channels), groups, spatial,
        float(scale), float(eps), float(momentum), cumulative);
    C10_CUDA_KERNEL_LAUNCH_CHECK();

    return output;
}
"""

_extension = None


def _get_extension():
    global _extension
    if _extension is None:
        _extension = load_inline(
            name="spectral_transpose3d_tensorcore_bn_pool_v5",
            cpp_sources=_CPP,
            cuda_sources=_CUDA,
            functions=["pack_spectrum", "tensorcore_bn_pool"],
            extra_cflags=["-O3"],
            extra_cuda_cflags=["-O3"],
            verbose=False,
        )
    return _extension


class ModelNew(nn.Module):
    def __init__(
        self,
        in_channels,
        out_channels,
        kernel_size,
        scale_factor,
        eps=1e-5,
        momentum=0.1,
    ):
        super().__init__()
        self.conv_transpose = nn.ConvTranspose3d(
            in_channels, out_channels, kernel_size
        )
        self.scale_factor = scale_factor
        self.batch_norm = nn.BatchNorm3d(
            out_channels, eps=eps, momentum=momentum
        )
        self.global_avg_pool = nn.AdaptiveAvgPool3d((1, 1, 1))
        self._spectral_cache = None

    def forward(self, x):
        conv = self.conv_transpose
        bn = self.batch_norm
        weight = conv.weight

        tensors = (
            weight,
            conv.bias,
            bn.weight,
            bn.bias,
            bn.running_mean,
            bn.running_var,
        )
        supported = (
            x.is_cuda
            and x.dtype == torch.float32
            and x.ndim == 5
            and x.numel() > 0
            and x.shape[1] == conv.in_channels
            and not torch.is_autocast_enabled()
            and conv.groups == 1
            and conv.stride == (1, 1, 1)
            and conv.padding == (0, 0, 0)
            and conv.dilation == (1, 1, 1)
            and conv.output_padding == (0, 0, 0)
            and all(
                tensor is not None
                and tensor.device == x.device
                and tensor.dtype == torch.float32
                and tensor.is_contiguous()
                for tensor in tensors
            )
        )

        if not supported:
            return self.global_avg_pool(bn(conv(x) * self.scale_factor))

        output_shape = tuple(
            size + kernel - 1
            for size, kernel in zip(x.shape[2:], conv.kernel_size)
        )
        spatial = output_shape[0] * output_shape[1] * output_shape[2]

        # In evaluation mode, all operations following the convolution are
        # affine, so only the convolution's DC coefficient is needed.
        if not bn.training:
            input_sums = x.sum(dim=(2, 3, 4))
            weight_sums = weight.sum(dim=(2, 3, 4))
            means = (input_sums @ weight_sums) / spatial
            means = (means + conv.bias) * self.scale_factor
            result = (
                (means - bn.running_mean)
                * torch.rsqrt(bn.running_var + bn.eps)
                * bn.weight
                + bn.bias
            )
            return result[:, :, None, None, None]

        fast_training = (
            x.shape[0] == 16
            and conv.in_channels % 16 == 0
            and conv.out_channels % 16 == 0
            and bn.num_batches_tracked is not None
            and bn.num_batches_tracked.device == x.device
            and torch.cuda.get_device_capability(x.device)[0] >= 7
        )

        if not fast_training:
            return self.global_avg_pool(bn(conv(x) * self.scale_factor))

        extension = _get_extension()
        cache_key = (
            id(weight),
            weight.data_ptr(),
            weight._version,
            tuple(weight.shape),
            tuple(weight.stride()),
            weight.device,
            output_shape,
        )

        with torch.no_grad():
            cache = self._spectral_cache
            if cache is None or cache[0] != cache_key:
                spectrum = torch.fft.rfftn(
                    weight, s=output_shape, dim=(2, 3, 4)
                )
                packed_weight, weight_dc = extension.pack_spectrum(
                    spectrum, 256.0
                )
                self._spectral_cache = (
                    cache_key,
                    weight,
                    packed_weight,
                    weight_dc,
                )
                del spectrum
            else:
                packed_weight, weight_dc = cache[2], cache[3]

            spectrum = torch.fft.rfftn(
                x, s=output_shape, dim=(2, 3, 4)
            )
            packed_input, input_dc = extension.pack_spectrum(
                spectrum, 1.0 / 32.0
            )
            del spectrum

            return extension.tensorcore_bn_pool(
                packed_input,
                packed_weight,
                input_dc,
                weight_dc,
                conv.bias,
                bn.weight,
                bn.bias,
                bn.running_mean,
                bn.running_var,
                bn.num_batches_tracked,
                conv.in_channels,
                conv.out_channels,
                spatial,
                output_shape[2],
                float(self.scale_factor),
                float(bn.eps),
                float(bn.momentum) if bn.momentum is not None else 0.0,
                bn.momentum is None,
            )
```
Result of evaluating it:
The solution is correct on the test inputs. Mean runtime 2.21 ms; the PyTorch reference (with TF32 enabled) takes 12.8 ms; speedup 5.792x.
Profiler, top device kernels of one forward pass of your solution:
| device kernel | total device time (us) | calls |
|---|---|---|
| tensorcore_energy_kernel(__half const*, __half const*, float*, int, int, int, int) | 1117.8 | 1 |
| pack_kernel(float2 const*, __half*, float*, int, int, int, int, int, long, long, long, lon | 193.7 | 1 |
| void regular_fft<20u, EPT<4u, 5u>, 32u, 12u, (padding_t)14, (twiddle_t)0, (loadstore_modif | 176.9 | 1 |
| void regular_fft<36u, EPT<6u>, 32u, 8u, (padding_t)6, (twiddle_t)0, (loadstore_modifier_t) | 175.3 | 1 |
| void vector_fft_r2c<36u, EPT<6u>, 16u, 21u, (padding_t)6, (twiddle_t)0, (loadstore_modifie | 162.6 | 1 |
| void at::native::elementwise_kernel<128, 2, at::native::gpu_kernel_impl_nocast<at::native: | 134.1 | 1 |
| void at::native::vectorized_elementwise_kernel<4, at::native::FillFunctor<float>, std::arr | 67.5 | 1 |
| finish_kernel(float const*, float const*, double const*, float const*, float const*, float | 62.7 | 1 |

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.
