The RegNetX32 model can be implemented programmatically by subclassing the generic AnyNet architecture and supplying its specific hyperparameters. The model initialization establishes $32$ stem channels, a group width of $16$, and a bottleneck ratio of $1$. The body of the network is configured through an architectural tuple defining the depths and channel widths of its stages, specifically using depths of $(4, 6)$ and channels of $(32, 80)$ for the first two stages. This setup automates the construction of the network's sequential blocks.

PyTorch Implementation:

class RegNetX32(AnyNet):
    def __init__(self, lr=0.1, num_classes=10):
        stem_channels, groups, bot_mul = 32, 16, 1
        depths, channels = (4, 6), (32, 80)
        super().__init__(
            ((depths[0], channels[0], groups, bot_mul),
             (depths[1], channels[1], groups, bot_mul)),
            stem_channels, lr, num_classes)

MXNet Implementation:

class RegNetX32(AnyNet):
    def __init__(self, lr=0.1, num_classes=10):
        stem_channels, groups, bot_mul = 32, 16, 1
        depths, channels = (4, 6), (32, 80)
        super().__init__(
            ((depths[0], channels[0], groups, bot_mul),
             (depths[1], channels[1], groups, bot_mul)),
            stem_channels, lr, num_classes)

JAX Implementation:

class RegNetX32(AnyNet):
    lr: float = 0.1
    num_classes: int = 10
    stem_channels: int = 32
    arch: tuple = ((4, 32, 16, 1), (6, 80, 16, 1))

TensorFlow Implementation:

class RegNetX32(AnyNet):
    def __init__(self, lr=0.1, num_classes=10):
        stem_channels, groups, bot_mul = 32, 16, 1
        depths, channels = (4, 6), (32, 80)
        super().__init__(
            ((depths[0], channels[0], groups, bot_mul),
             (depths[1], channels[1], groups, bot_mul)),
            stem_channels, lr, num_classes)