Based on your code snippet it seems your temporal dimension has only a single value which would also explain the kernel size of 1 in the conv layer. In that case, this operation should be equal to a linear layer. Also the pooling layer won’t have any effect with a kernel size of 1 as seen here:
x = torch.randn(2, 16, 24)
pool = nn.MaxPool1d(1)
out = pool(x)
print((x == out).all())
> tensor(True)

so you can remove it.