A layer summary of the RegNetX32 architecture illustrates its progressive downsampling and channel expansion strategy. Given a single-channel input image of size $$96 	imes 96$$, the network's sequential stages process the data into feature maps with decreasing spatial dimensions and increasing channel counts. The tensor shapes transition sequentially from $$(1, 32, 48, 48)$$ to $$(1, 32, 24, 24)$$, and then to $$(1, 80, 12, 12)$$, before a final global average pooling and dense layer produce the $$(1, 10)$$ classification output.

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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:
```python
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:
```python
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:
```python
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:
```python
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)
```

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