PyTorch eager

import torch
import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    """
    Model that performs:
    1. Conv3D
    2. HardSwish activation
    3. GroupNorm  
    4. Mean pooling across spatial dimensions
    """
    def __init__(self, in_channels, out_channels, kernel_size, num_groups=4, bias=True):
        super(Model, self).__init__()
        self.conv = nn.Conv3d(in_channels, out_channels, kernel_size, bias=bias)
        self.group_norm = nn.GroupNorm(num_groups, out_channels)

    def forward(self, x):
        x = self.conv(x)                             # (B, C, D, H, W)
        x = F.hardswish(x)                           # Nonlinear activation
        x = self.group_norm(x)                       # Normalization over channels
        x = torch.mean(x, dim=[2, 3, 4])             # Mean over spatial dims → (B, C)
        return x

# === Test config ===
batch_size = 1024
in_channels = 3
out_channels = 16
depth, height, width = 16, 32, 32
kernel_size = 4

def get_inputs():
    return [torch.rand(batch_size, in_channels, depth, height, width)]

def get_init_inputs():
    return [in_channels, out_channels, kernel_size]