Feature maps are the intermediate outputs of convolutional neural networks (CNNs), and you often need to predict their spatial dimensions to design architectures correctly. In this question, you’ll compute the output height/width of a 2D convolution feature map given input size, kernel size, stride, and padding.

The feature map size is defined as:
$$
H_{out} = \left\lfloor \frac{H_{in} + 2P - K}{S} \right\rfloor + 1,\quad
W_{out} = \left\lfloor \frac{W_{in} + 2P - K}{S} \right\rfloor + 1
$$

## Requirements

Implement the function
```python
import numpy as np

def conv2d_feature_map_dims(h_in, w_in, k, s, p):
    \"""
    Computes the spatial dimensions (height, width) of a 2D convolution output feature map.

    Args:
        h_in (int): Input height H_in.
        w_in (int): Input width W_in.
        k (int): Kernel size K (square kernel).
        s (int): Stride S.
        p (int): Padding P (applied to all sides).

    Returns:
        np.ndarray: Array of shape (2,) [h_out, w_out].
    \"""
    # Your solution below
```

**Rules:**
- Use the formula above with floor division behavior.
- Return a NumPy array of shape (2,).
- Do not use any deep learning frameworks (no PyTorch/TensorFlow).
- You may use only NumPy and built-in Python libraries.

## Example

```python
h_in = 32
w_in = 32
k = 3
s = 1
p = 1

conv2d_feature_map_dims(h_in, w_in, k, s, p)
```

Output:
```python
array([32, 32])
```

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Feature map dimensions

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117. Feature map dimensions

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Similar Questions

Argument	Type
k	int
p	int
s	int
h_in	int
w_in	int