import torch


def preprocess_data(data, skip=False):
    # Assuming 'data' is a tensor of shape [n_samples, 3]
    if not skip:
        # Compute argmin and argmax for each row
        argmin_values = torch.argmin(data, dim=1, keepdim=True).float()
        argmax_values = torch.argmax(data, dim=1, keepdim=True).float()

        # Normalize or scale argmin and argmax if necessary
        # For example, here I am just dividing by the number of features
        argmin_values /= data.shape[1] - 1
        argmax_values /= data.shape[1] - 1

        # Concatenate the argmin and argmax values to the original data
        new_data = torch.cat((data, argmin_values, argmax_values), dim=1)
    else:
        new_data = data
    return new_data


PURE_RGB = preprocess_data(
    torch.tensor([[1, 0, 0], [0, 1, 0], [0, 0, 1]], dtype=torch.float32)
)