code: def targets_one_hot_default_collator(batch, num_classes: int):
      """
      The collators collates the batch for the following input:
  
      Input:
          input : [[img0, ..., imgk]]
          label:  [
                      [[1, 3, 6], [4, 9]]
                      [[1, 5], [6, 8, 10, 11]]
                      .....
                  ]
      Output:
          output: [img0, img0, .....,]
          label: [[0, 1, 0, 1, ..., -1, 0, 0, 1], [0, 1, 0, 0, 0, 1, 0], ....]
      """
      assert (
          num_classes > 0
      ), "num_classes not specified for the collator"
      assert "data" in batch[0], "data not found in sample"
      assert "label" in batch[0], "label not found in sample"
      assert len(batch[0]["data"]) == 1, (
          "This collator supports only 1 data source. "
          "Please extend it to support many data sources."
      )
      assert len(batch[0]["label"][0]) == 2, (
          "This collator takes positive and negative labels separately. "
          "Please modify it to suit your needs."
      )
  
      data = torch.stack([x["data"][0] for x in batch])
      data_valid = torch.stack(
          [torch.tensor(x["data_valid"][0]) for x in batch]
      )
      data_idx = torch.stack(
          [torch.tensor(x["data_idx"][0]) for x in batch]
      )
  
      labels = [x["label"][0] for x in batch]
      output_labels = []
      for idx in range(data.shape[0]):
          output_labels.append(
              convert_to_one_hot(
                  labels[idx][0], labels[idx][1], num_classes
              )
          )
      output_batch = {
          "data": [data],
          "label": [torch.stack(output_labels)],
          "data_valid": [data_valid],
          "data_idx": [data_idx],
      }
      return output_batch
  
imports: import torch
  from vissl.data.collators import register_collator
label_desc: Use image node_id 2 for calling a global function with example usage: targets_one_hot_default_collator(batch, num_classes) and returns: output_batch