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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def euclidean_random_projection_split(data, indices, rng_state):
 """Given a set of ``indices`` for data points from ``data``, create a random hyperplane to split the data, returning two arrays indices that fall on either side of the hyperplane. This is the basis for a random projection tree, which simply uses this splitting recursively. This particular split uses euclidean distance to determine the hyperplane and which side each data sample falls on. Parameters data: array of shape (n_samples, n_features) The original data to be split indices: array of shape (tree_node_size,) The indices of the elements in the ``data`` array that are to be split in the current operation. rng_state: array of int64, shape (3,) The internal state of the rng Returns ------- indices_left: array The elements of ``indices`` that fall on the "left" side of the random hyperplane. indices_right: array The elements of ``indices`` that fall on the "left" side of the random hyperplane. """

    dim = data.shape[1]

    # Select two random points, set the hyperplane between them
    left_index = tau_rand_int(rng_state) % indices.shape[0]
    right_index = tau_rand_int(rng_state) % indices.shape[0]
    right_index += left_index == right_index
    right_index = right_index % indices.shape[0]
    left = indices[left_index]
    right = indices[right_index]

    # Compute the normal vector to the hyperplane (the vector between
    # the two points) and the offset from the origin
    hyperplane_offset = 0.0
    hyperplane_vector = np.empty(dim, dtype=np.float32)

    for d in range(dim):
        hyperplane_vector[d] = data[left, d] - data[right, d]
        hyperplane_offset -= (
            hyperplane_vector[d] * (data[left, d] + data[right, d]) / 2.0
        )

    # For each point compute the margin (project into normal vector, add offset)
    # If we are on lower side of the hyperplane put in one pile, otherwise
    # put it in the other pile (if we hit hyperplane on the nose, flip a coin)
    n_left = 0
    n_right = 0
    side = np.empty(indices.shape[0], np.int8)
    for i in range(indices.shape[0]):
        margin = hyperplane_offset
        for d in range(dim):
            margin += hyperplane_vector[d] * data[indices[i], d]

        if margin == 0:
            side[i] = tau_rand_int(rng_state) % 2
            if side[i] == 0:
                n_left += 1
            else:
                n_right += 1
        elif margin > 0:
            side[i] = 0
            n_left += 1
        else:
            side[i] = 1
            n_right += 1

    # Now that we have the counts allocate arrays
    indices_left = np.empty(n_left, dtype=np.int64)
    indices_right = np.empty(n_right, dtype=np.int64)

    # Populate the arrays with indices according to which side they fell on
    n_left = 0
    n_right = 0
    for i in range(side.shape[0]):
        if side[i] == 0:
            indices_left[n_left] = indices[i]
            n_left += 1
        else:
            indices_right[n_right] = indices[i]
            n_right += 1

    return indices_left, indices_right, hyperplane_vector, hyperplane_offset