//  [python]
def print_diagnostics(self):
        """Print diagnostics for each dimension of the tensor."""
        if self.stats is None:
            print(f"Warning: the stats of {self.name} is None.")
            return
        for dim, this_dim_stats in enumerate(self.stats):
            for stats_type, stats_list in this_dim_stats.items():
                # stats_type could be "rms", "value", "abs", "eigs", "positive".
                # "value" could be a list of TensorAndCount, or None
                if stats_list is None:
                    assert stats_type == "eigs"
                    continue

                if stats_type == "eigs":
                    assert len(stats_list) == 1
                    stats = stats_list[0].tensor / stats_list[0].count
                    try:
                        eigs, _ = torch.linalg.eigh(stats)
                        stats = eigs.abs().sqrt()
                    except:  # noqa
                        print(
                            "Error getting eigenvalues, trying another method."
                        )
                        eigs = torch.linalg.eigvals(stats)
                        stats = eigs.abs().sqrt()
                        # sqrt so it reflects data magnitude, like stddev- not variance
                elif len(stats_list) == 1:
                    stats = stats_list[0].tensor / stats_list[0].count
                else:
                    stats = torch.cat(
                        [x.tensor / x.count for x in stats_list], dim=0
                    )

                if stats_type == "rms":
                    # we stored the square; after aggregation we need to take sqrt.
                    stats = stats.sqrt()

                # if `summarize` we print percentiles of the stats; else,
                # we print out individual elements.
                summarize = (
                    len(stats_list) > 1
                ) or self.opts.dim_is_summarized(stats.numel())
                if summarize:  # usually `summarize` will be true
                    # print out percentiles.
                    stats = stats.sort()[0]
                    num_percentiles = 10
                    size = stats.numel()
                    percentiles = []
                    for i in range(num_percentiles + 1):
                        index = (i * (size - 1)) // num_percentiles
                        percentiles.append(stats[index].item())
                    percentiles = ["%.2g" % x for x in percentiles]
                    percentiles = " ".join(percentiles)
                    ans = f"percentiles: [{percentiles}]"
                else:
                    ans = stats.tolist()
                    ans = ["%.2g" % x for x in ans]
                    ans = "[" + " ".join(ans) + "]"
                if stats_type == "value":
                    # This norm is useful because it is strictly less than the largest
                    # sqrt(eigenvalue) of the variance, which we print out, and shows,
                    # speaking in an approximate way, how much of that largest eigenvalue
                    # can be attributed to the mean of the distribution.
                    norm = (stats ** 2).sum().sqrt().item()
                    ans += f", norm={norm:.2g}"
                mean = stats.mean().item()
                rms = (stats ** 2).mean().sqrt().item()
                ans += f", mean={mean:.2g}, rms={rms:.2g}"

                # OK, "ans" contains the actual stats, e.g.
                # ans = "percentiles: [0.43 0.46 0.48 0.49 0.49 0.5 0.51 0.52 0.53 0.54 0.59], mean=0.5, rms=0.5"

                sizes = [x.tensor.shape[0] for x in stats_list]
                size_str = (
                    f"{sizes[0]}"
                    if len(sizes) == 1
                    else f"{min(sizes)}..{max(sizes)}"
                )
                print(
                    f"module={self.name}, dim={dim}, size={size_str}, {stats_type} {ans}"
                )