TensorFlow to PyTorch -- Is this the best way to do it with latest PyTorch syntax?

I’m trying to convert a MMD-VAE implementation from TensorFlow to PyTorch. I’ve got most of the model built just fine but I just want to make sure that I am converting the following functions correctly (Everything is working but I’m not getting the results I expect so I thought maybe I am computing the kernel incorrectly as I am not so strong in TensorFlow).

In TensorFlow:

def compute_kernel(x, y):
    x_size = tf.shape(x)[0]
    y_size = tf.shape(y)[0]
    dim = tf.shape(x)[1]
    tiled_x = tf.tile(tf.reshape(x, tf.stack([x_size, 1, dim])), tf.stack([1, y_size, 1]))
    tiled_y = tf.tile(tf.reshape(y, tf.stack([1, y_size, dim])), tf.stack([x_size, 1, 1]))
    return tf.exp(-tf.reduce_mean(tf.square(tiled_x - tiled_y), axis=2) / tf.cast(dim, tf.float32))

And what I have for PyTorch:

def compute_kernel(x, y):
    x_size = x.size(0)
    y_size = y.size(0)
    dim = x.size(1)
    x = x.unsqueeze(1) # (x_size, 1, dim)
    y = y.unsqueeze(0) # (1, y_size, dim)
    tiled_x = x.expand(x_size, y_size, dim)
    tiled_y = y.expand(x_size, y_size, dim)
    kernel_input = (tiled_x - tiled_y).pow(2).mean(2)/float(dim)
    return torch.exp(-kernel_input)

Thanks for your help!!