Implement 2D convolution forward pass

## Problem Implement the **forward pass of a 2D convolution (conv2d)** from scratch (no deep learning libraries). You are given: - Input tensor `x` with shape **(N, C_in, H, W)** (NCHW layout) - Filter weights `w` with shape **(C_out, C_in, K_h, K_w)** - Optional bias `b` with shape **(C_out,)** (may be `None`) - Integer stride `s_h, s_w` - Integer padding `p_h, p_w` (zero-padding applied to height/width) Compute the output tensor `y` with shape **(N, C_out, H_out, W_out)** where: \[ H_{out} = \left\lfloor \frac{H + 2p_h - K_h}{s_h} \right\rfloor + 1, \quad W_{out} = \left\lfloor \frac{W + 2p_w - K_w}{s_w} \right\rfloor + 1 \] For each output element: \[ y[n, c_{out}, i, j] = \sum_{c_{in}=0}^{C_{in}-1} \sum_{u=0}^{K_h-1} \sum_{v=0}^{K_w-1} \; x_{pad}[n, c_{in}, i\cdot s_h + u, j\cdot s_w + v] \cdot w[c_{out}, c_{in}, u, v] + b[c_{out}] \] where `x_pad` is `x` padded with zeros by `(p_h, p_w)`. ## Requirements - Return `y` as a dense numeric array/tensor. - Do not use existing convolution operators. - Handle edge cases such as `b is None`, non-square kernels, and different strides/padding. ## Constraints (typical for an interview unit test) - Sizes are small enough that an \(O(N\cdot C_{out}\cdot C_{in}\cdot H_{out}\cdot W_{out}\cdot K_h\cdot K_w)\) implementation passes. - Inputs are floating point numbers. ## Example (shape check) If `x` is `(1, 3, 32, 32)`, `w` is `(8, 3, 3, 3)`, stride `(1,1)`, padding `(1,1)`, then output shape is `(1, 8, 32, 32)`.