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:  

python
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:   
python
import torch
from torch.utils.cpp_extension import load_inline

… CUDA C++ source code for the kernel …
relu_source = """""
…
""""

relu_cpp_source = """""
torch::Tensor relu_cuda(torch::Tensor x);
""""

Compile the inline CUDA code
relu = load_inline(
name="relu",
cpp_sources=relu_cpp_source,
cuda_sources=relu_source,
functions=["relu_cuda"],
verbose=True
)

class ModelNew(torch.nn.Module):
def init(self):
super(ModelNew, self).init()
self.relu = relu # The module containing the kernel

def forward(self, x):
    return self.relu.relu_cuda(x)


You are given the following architecture:   
  
python
import torch
import torch.nn as nn

class Model(nn.Module):
"""
Canberra Focal Loss implementation.
Computes the Canberra distance with focal loss modulation between two sets of vectors.
"""
def init(self, alpha=1.0, gamma=2.0):
super(Model, self).init()
self.alpha = alpha
self.gamma = gamma

def forward(self, x: torch.Tensor, y: torch.Tensor, target: torch.Tensor) -> torch.Tensor:  
    """  
    Compute Canberra Focal Loss between x and y.

    Args:  
        x (torch.Tensor): First set of vectors [batch_size, feature_dim]
        y (torch.Tensor): Second set of vectors [batch_size, feature_dim]
        target (torch.Tensor): Target weights [batch_size]

    Returns:  
        torch.Tensor: Canberra Focal Loss [batch_size]
    """  
    # Input validation
    if x.shape != y.shape:
        raise ValueError(f"Input tensors must have the same shape, got {x.shape} and {y.shape}")
    
    if x.dim() != 2:
        raise ValueError(f"Input tensors must be 2D, got {x.dim()}D")
    
    # Compute element-wise Canberra distance terms
    diff = torch.abs(x - y)
    denom = torch.abs(x) + torch.abs(y)
    canberra_terms = torch.where(denom > 0, diff / denom, torch.zeros_like(diff))
    
    # Apply focal modulation element-wise using built-in functions
    focal_weights = self.alpha * torch.pow(1.0 + canberra_terms, -self.gamma)
    focal_terms = focal_weights * canberra_terms
    
    # Sum along feature dimension using built-in function
    focal_loss = torch.sum(focal_terms, dim=1)
    
    return focal_loss

batch_size = 256
feature_dim = 1024

def get_inputs():
# Generate two sets of positive vectors (to avoid sign issues)
x = torch.abs(torch.randn(batch_size, feature_dim)) + 0.1
y = torch.abs(torch.randn(batch_size, feature_dim)) + 0.1
target = torch.ones(batch_size)
return [x, y, target]

def get_init_inputs():
return [1.0, 2.0]



Your task is to write a new file `canberra_focalloss_cudacode.py` that defines a new model `ModelNew` which uses a custom CUDA kernel to accelerate the Canberra Focal Loss calculation. The goal is to achieve a significant speedup while maintaining numerical precision.

The recommended implementation strategy is to use a **parallel reduction pattern**:
1.  Launch one thread block for each sample in the batch (`batch_size` number of blocks).
2.  Within each block, have multiple threads collaborate to compute the sum for that single sample.
3.  Each thread should iterate over the feature dimension with a stride equal to the block size, accumulating a partial sum.
4.  For each element, compute the Canberra distance term: |x_i - y_i| / (|x_i| + |y_i|), handling the case where denominator is 0.
5.  Apply focal modulation element-wise: alpha * (1 + canberra_term)^(-gamma) * canberra_term.
6.  Use shared memory to store these partial sums and then perform a standard parallel reduction to get the final loss for that sample.
7.  The first thread of the block should write the final result to the output tensor.

The implementation should be robust, handle input validation, and use `extra_cuda_cflags` like `"-O3"` and `"--use_fast_math"` for performance. The final output should be a single python file containing the CUDA kernel, the compilation logic via `load_inline`, and the new `ModelNew` class. Make sure to include proper input validation in the CUDA function using TORCH_CHECK macros.
