dists = np.sum(np.square((points[query_idx] - pick_point).astype(np.float32)), axis=1)

This line of code calculates the Euclidean distance between a query point and all other points in a dataset.

Here's a breakdown of what's happening:

- `points[query_idx]` selects the query point from the dataset, using its index `query_idx`.
- `(points[query_idx] - pick_point)` subtracts the coordinates of `pick_point` from the query point's coordinates, giving us a vector pointing from `pick_point` to the query point.
- `np.square(...)` squares each element in the vector, effectively calculating the squared distance between the two points in each dimension.
- `np.sum(..., axis=1)` sums the squared distances across all dimensions (i.e. x, y, z), resulting in a single value for each point in the dataset. This gives us a measure of how far each point is from the query point, in terms of Euclidean distance.

The resulting array `dists` contains these distances for all points in the dataset. We can use this array to find the k nearest neighbors to the query point, for example by sorting it and selecting the first k elements.