How to make a 2D Gaussian Filter in Tensorflow?

First define a normalized 2D gaussian kernel:

def gaussian_kernel(size: int,
                    mean: float,
                    std: float,
                   ):
    """Makes 2D gaussian Kernel for convolution."""

    d = tf.distributions.Normal(mean, std)

    vals = d.prob(tf.range(start = -size, limit = size + 1, dtype = tf.float32))

    gauss_kernel = tf.einsum('i,j->ij',
                                  vals,
                                  vals)

    return gauss_kernel / tf.reduce_sum(gauss_kernel)

Next, use tf.nn.conv2d to convolve this kernel with an image:

# Make Gaussian Kernel with desired specs.
gauss_kernel = gaussian_kernel( ... )

# Expand dimensions of `gauss_kernel` for `tf.nn.conv2d` signature.
gauss_kernel = gauss_kernel[:, :, tf.newaxis, tf.newaxis]

# Convolve.
tf.nn.conv2d(image, gauss_kernel, strides=[1, 1, 1, 1], padding="SAME")

Tensorflow addons includes a 2D Gaussian blur. This is the function signature:

@tf.function
tfa.image.gaussian_filter2d(
    image: tfa.types.TensorLike,
    filter_shape: Union[List[int], Tuple[int], int] = [3, 3],
    sigma: Union[List[float], Tuple[float], float] = 1.0,
    padding: str = 'REFLECT',
    constant_values: tfa.types.TensorLike = 0,
    name: Optional[str] = None
) -> tfa.types.TensorLike