Write a custom CUDA kernel to optimize `torch.angle` for complex tensors.

The operation computes the element-wise angle (phase) of a complex tensor. For a complex number z = x + iy, angle(z) = atan2(y, x).

Problem Analysis:
This is a memory-bound element-wise operation.
1. Data Layout: Input is `complex64` (8 bytes per element: real, imag), Output is `float32` (4 bytes per element).
2. Throughput: The arithmetic intensity is low (one transcendental function per 8 bytes loaded). Performance is strictly limited by how fast data can be moved between VRAM and registers.
3. Access Pattern: Standard floating point loads might read 4 bytes at a time. Optimizing this to wider transactions is key.

Optimization Strategy: Vectorized Access (2x Elements per Thread)

1. Vectorized Loads (Float4): 
   - Each `complex64` consists of two `float`s (real, imag).
   - Using `float4` loads allows a single thread to load **two** `complex64` elements at once (16 bytes).
   - Layout in `float4`: `x`=real1, `y`=imag1, `z`=real2, `w`=imag2.

2. Vectorized Stores (Float2):
   - The result for two complex inputs is two float outputs.
   - We can pack these into a `float2` (8 bytes) and store them in a single instruction.

3. Fast Math:
   - Use `atan2f` for computation.
   - The grid-stride loop handles the bulk of data using vectorized paths and a scalar tail loop for remaining elements.
  
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

# Use Complex64 input
BATCH_SIZE = 4096
DIM = 4096
SHAPE = (BATCH_SIZE, DIM)

class AngleModel(nn.Module):
    def __init__(self):
        super(AngleModel, self).__init__()

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return torch.angle(x)

class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.act = AngleModel()
    
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return self.act(x)

def get_inputs():
    # randn generates complex numbers if dtype is complex
    x = torch.randn(SHAPE, dtype=torch.complex64)
    return [x.contiguous()]

def get_init_inputs():
    return []