implementation of a Python function that implements the RANSAC algorithm to perform plane fitting. Plane fitting is the process of finding the best-fit plane that passes through a set of points. RANSAC (Random Sample Consensus) is a popular algorithm used for robust plane fitting in the presence of outliers. The RANSAC function will take the following inputs: 1. points: A 3D numpy array containing the x, y, and coordinates of the points to fit a plane to. 2. num-iterations: The number of iterations to run the RANSAC algorithm. 3. distance-threshold: The maximum distance between a point and the plane for it to be consid- ered an inlier. 4. min-inliers: The minimum number of inliers required for a plane to be considered a good fit. Inside the function, you will: - Randomly sample 3 points from the input data. Fit a plane to the sample using the least square fit method. - Calculate the distance between each point and the plane. Count the number of inliers. Update the best plane if this iteration produced a better fit. Return the best-fit plane. . - - - Tasks (a) Using Python, load and plot the given 3D point cloud.