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  my $includes_background_class = $probabilities_batched->dim(0) == IMAGENET_LABEL_COUNT_WITH_BG;
  
  if( IN_IPERL ) {
      my $html = IPerl->html(
          my_table( [0..$#image_names], sub {
              my ($batch_idx, $h) = @_;
              my $image_name = $image_names[$batch_idx];
              my @top_for_image = $top_lists[$batch_idx]->list;
              (
                      $h->tt($image_name),
                      $h->a( { href => $images_for_test_to_uri{$image_name} },
                          $h->img({
                              src => $images_for_test_to_uri{$image_name},
                              alt => $image_name,
                              width => '50%',
                          })
                      ),
                      do {
                          my @tr;
                          push @tr, [ $h->th('Rank', 'Label No', 'Label', 'Prob') ];
                          while( my ($i, $label_index) = each @top_for_image ) {
                              my $class_index = $includes_background_class ? $label_index : $label_index + 1;
                              push @tr, [ $h->td(
                                      $i + 1,
                                      $class_index,
                                      $labels[$class_index],
                                      $probabilities_batched->at($label_index,$batch_idx),
                              ) ];
  
                          }
                          $h->table([$h->tr(@tr)])
                      },
                  )
          })
      );
      IPerl->display($html);
  } else {
      for my $batch_idx (0..$#image_names) {
          my $image_name = $image_names[$batch_idx];
          my @top_for_image = $top_lists[$batch_idx]->list;
          my @td;
          say "Image name: `$image_name`";
          my $header = [ ('Rank', 'Label No', 'Label', 'Prob') ];
          my @rows;
          while( my ($i, $label_index) = each @top_for_image ) {
              my $class_index = $includes_background_class ? $label_index : $label_index + 1;
              push @rows, [ (
                      $i + 1,
                      $class_index,
                      $labels[$class_index],
                      $probabilities_batched->at($label_index,$batch_idx),
              ) ];
          }
          say generate_table( rows => [ $header, @rows ], header_row => 1 );
          print "\n";
      }
  }

B<DISPLAY>:

=for html <span style="display:inline-block;margin-left:1em;"><p><table style="width: 100%"><tr><td><tt>apple</tt></td><td><a href="https://upload.wikimedia.org/wikipedia/commons/1/15/Red_Apple.jpg"><img alt="apple" src="https://upload.wikimedia.org/...

  my $p_approx_batched = $probabilities_batched->sumover->approx(1, 1e-5);
  p $p_approx_batched;
  say "All probabilities sum up to approximately 1" if $p_approx_batched->all->sclr;

B<STREAM (STDOUT)>:

  All probabilities sum up to approximately 1

B<STREAM (STDERR)>:

=for html <span style="display:inline-block;margin-left:1em;"><pre style="display: block"><code><span style="color: #cc66cc;">PDL</span><span style="color: #33ccff;"> {</span><span style="">
    </span><span style="color: #6666cc;">Data    </span><span style=""> : </span><span style="color: #33ccff;">[</span><span style="color: #ff6633;">1 1 1 1 1 1 1 1 1 1 1 1</span><span style="color: #33ccff;">]</span><span style="">
    </span><span style="color: #6666cc;">Type    </span><span style=""> : </span><span style="color: #cc66cc;">double</span><span style="">
    </span><span style="color: #6666cc;">Shape   </span><span style=""> : </span><span style="color: #33ccff;">[</span><span style="color: #9999cc;">12</span><span style="color: #33ccff;">]</span><span style="">
    </span><span style="color: #6666cc;">Nelem   </span><span style=""> : </span><span style="color: #dd6;">12</span><span style="">
    </span><span style="color: #6666cc;">Min     </span><span style=""> : </span><span style="color: #f66;">1</span><span style="">
    </span><span style="color: #6666cc;">Max     </span><span style=""> : </span><span style="color: #99f;">1</span><span style="">
    </span><span style="color: #6666cc;">Badflag </span><span style=""> : </span><span style="color: #2c2;">No</span><span style="">
    </span><span style="color: #6666cc;">Has Bads</span><span style=""> : </span><span style="color: #2c2;">No</span><span style="">
</span><span style="color: #33ccff;">}</span><span style="">
</span></code></pre></span>

B<RESULT>:

  1

=head1 RESOURCE USAGE

  use Filesys::DiskUsage qw/du/;
  
  my $total = du( { 'human-readable' => 1, dereference => 1 },
      $model_archive_path, $model_base, $labels_path );
  
  say "Disk space usage: $total"; undef;

B<STREAM (STDOUT)>:

  Disk space usage: 27.45M

=head1 DEBUGGING

The following images can be used to test the C<load_image_to_pdl> function.

  my @solid_channel_uris = (
      'https://upload.wikimedia.org/wikipedia/commons/thumb/6/62/Solid_red.svg/480px-Solid_red.svg.png',
      'https://upload.wikimedia.org/wikipedia/commons/thumb/1/1d/Green_00FF00_9x9.svg/480px-Green_00FF00_9x9.svg.png',
      'https://upload.wikimedia.org/wikipedia/commons/thumb/f/ff/Solid_blue.svg/480px-Solid_blue.svg.png',
  );
  undef;

=head1 CPANFILE

  requires 'AI::TensorFlow::Libtensorflow';
  requires 'AI::TensorFlow::Libtensorflow::DataType';
  requires 'Archive::Extract';
  requires 'Data::Printer';
  requires 'Data::Printer::Filter::PDL';
  requires 'FFI::Platypus::Buffer';
  requires 'FFI::Platypus::Memory';