```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>
#include <vector>

std::vector<torch::Tensor> pack_channels_last_cuda(
    torch::Tensor input,
    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>
#include <vector>

constexpr int CHANNELS = 64;
constexpr int TILE = 64;
constexpr int THREADS = 256;

__global__ __launch_bounds__(THREADS)
void pack_input(
    const float* __restrict__ input,
    float* __restrict__ output,
    int64_t spatial)
{
    __shared__ float tile[TILE][TILE + 1];

    const int tid = threadIdx.x;
    const int64_t position_base = int64_t(blockIdx.x) * TILE;
    const int64_t batch_base = int64_t(blockIdx.y) * CHANNELS * spatial;

    #pragma unroll
    for (int i = tid; i < CHANNELS * TILE; i += THREADS) {
        const int channel = i / TILE;
        const int position = i % TILE;
        const int64_t p = position_base + position;
        tile[channel][position] =
            p < spatial ? input[batch_base + channel * spatial + p] : 0.0f;
    }

    __syncthreads();

    #pragma unroll
    for (int i = tid; i < TILE * CHANNELS; i += THREADS) {
        const int position = i / CHANNELS;
        const int channel = i % CHANNELS;
        const int64_t p = position_base + position;
        if (p < spatial) {
            output[batch_base + p * CHANNELS + channel] =
                tile[channel][position];
        }
    }
}

// Preserve the transposed-convolution filter orientation while changing
// its physical storage from [CI, CO, KH, KW] to [CI, KH, KW, CO].
__global__ void pack_weight(
    const float* __restrict__ input,
    float* __restrict__ output)
{
    const int index = blockIdx.x * blockDim.x + threadIdx.x;
    if (index < CHANNELS * CHANNELS * 9) {
        const int co = index % CHANNELS;
        const int tap = (index / CHANNELS) % 9;
        const int ci = index / (CHANNELS * 9);
        output[index] = input[(ci * CHANNELS + co) * 9 + tap];
    }
}

std::vector<torch::Tensor> pack_channels_last_cuda(
    torch::Tensor input,
    torch::Tensor weight)
{
    TORCH_CHECK(input.is_cuda() && weight.is_cuda(),
                "Expected CUDA tensors");
    TORCH_CHECK(input.device() == weight.device(),
                "Input and weight must share a device");
    TORCH_CHECK(input.scalar_type() == at::kFloat &&
                weight.scalar_type() == at::kFloat,
                "Expected float32 tensors");
    TORCH_CHECK(input.dim() == 4 && input.size(1) == CHANNELS,
                "Expected 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(input.is_contiguous() ||
                input.is_contiguous(at::MemoryFormat::ChannelsLast),
                "Unsupported input layout");
    TORCH_CHECK(weight.is_contiguous() ||
                weight.is_contiguous(at::MemoryFormat::ChannelsLast),
                "Unsupported weight layout");
    TORCH_CHECK(input.size(0) > 0 && input.size(0) <= 65535 &&
                input.size(2) > 0 && input.size(3) > 0,
                "Unsupported input dimensions");

    c10::cuda::CUDAGuard guard(input.device());
    cudaStream_t stream = at::cuda::getCurrentCUDAStream();

    torch::Tensor packed_input = input;
    if (!input.is_contiguous(at::MemoryFormat::ChannelsLast)) {
        packed_input = torch::empty(
            input.sizes(),
            input.options().memory_format(at::MemoryFormat::ChannelsLast));

        const int64_t spatial = input.size(2) * input.size(3);
        const int64_t blocks = (spatial + TILE - 1) / TILE;
        TORCH_CHECK(blocks <= 2147483647LL, "Input is too large");

        pack_input<<<
            dim3(static_cast<unsigned>(blocks),
                 static_cast<unsigned>(input.size(0))),
            THREADS, 0, stream>>>(
                input.data_ptr<float>(),
                packed_input.data_ptr<float>(),
                spatial);
        C10_CUDA_KERNEL_LAUNCH_CHECK();
    }

    torch::Tensor packed_weight = weight;
    if (!weight.is_contiguous(at::MemoryFormat::ChannelsLast)) {
        packed_weight = torch::empty(
            weight.sizes(),
            weight.options().memory_format(at::MemoryFormat::ChannelsLast));

        pack_weight<<<
            (CHANNELS * CHANNELS * 9 + THREADS - 1) / THREADS,
            THREADS, 0, stream>>>(
                weight.data_ptr<float>(),
                packed_weight.data_ptr<float>());
        C10_CUDA_KERNEL_LAUNCH_CHECK();
    }

    return {packed_input, packed_weight};
}
"""

_extension = load_inline(
    name="transpose_conv64_channels_last_v6",
    cpp_sources=_cpp_source,
    cuda_sources=_cuda_source,
    functions=["pack_channels_last_cuda"],
    extra_cflags=["-O3"],
    extra_cuda_cflags=["-O3"],
    verbose=False,
)


class _PackChannelsLast(torch.autograd.Function):
    @staticmethod
    def forward(ctx, x, weight):
        packed_x, packed_weight = _extension.pack_channels_last_cuda(x, weight)
        return packed_x, packed_weight

    @staticmethod
    def backward(ctx, grad_x, grad_weight):
        # Packing changes storage layout, not logical tensor coordinates.
        return grad_x, grad_weight


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
        weight = conv.weight

        if (
            x.is_cuda
            and x.ndim == 4
            and x.dtype == torch.float32
            and weight.device == x.device
            and weight.dtype == torch.float32
            and 0 < x.shape[0] <= 65535
            and x.shape[1] == 64
            and x.shape[2] > 0
            and x.shape[3] > 0
            and x.shape[2] * x.shape[3] <= 2147483647
            and conv.in_channels == 64
            and conv.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 conv.dilation == (1, 1)
            and conv.groups == 1
            and (
                x.is_contiguous()
                or x.is_contiguous(memory_format=torch.channels_last)
            )
            and (
                weight.is_contiguous()
                or weight.is_contiguous(memory_format=torch.channels_last)
            )
            and (
                conv.bias is None
                or (
                    conv.bias.device == x.device
                    and conv.bias.dtype == torch.float32
                )
            )
            and torch.backends.cudnn.enabled
            and not torch.is_autocast_enabled()
        ):
            packed_x, packed_weight = _PackChannelsLast.apply(x, weight)
            return F.conv_transpose2d(
                packed_x,
                packed_weight,
                conv.bias,
                stride=conv.stride,
                padding=conv.padding,
                output_padding=conv.output_padding,
                groups=conv.groups,
                dilation=conv.dilation,
            )

        return conv(x)
```