Error merging 'cfg.yaml' with schema: Cannot merge DictConfig with ListConfig

Network: grounding_dino
Tag: nvcr.io/nvidia/tao/tao-toolkit:5.5.0-pyt
Config (could not upload the file):

dataset:
  batch_size: 4
  max_labels: 50
  test_data_sources: null
  train_data_sources:
    image_dir: /root/workspace/german-plates-roboflow/train/
    json_file: /root/workspace/german-plates-roboflow/annotations/odvg/train_only_license_cont_id_odvg.jsonl  # odvg format
    label_map:  /root/workspace/german-plates-roboflow/annotations/odvg/train_only_license_cont_id_odvg_labelmap.json
  val_data_sources:
    image_dir: /root/workspace/german-plates-roboflow/val/
    json_file: /root/workspace/german-plates-roboflow/annotations/contiguous_ids/val_only_license_cont_id.json  # category ids need to be contiguous
  workers: 4
model:
  dec_layers: 6
  dropout_ratio: 0
  enc_layers: 6
  num_feature_levels: 4
  num_queries: 900
  num_select: 300
  pretrained_backbone_path: null
  use_dn: true
results_dir: /root/workspace/experiments/exp_0/
train:
  activation_checkpoint: true
  distributed_strategy: ddp
  # is_dry_run: true
  num_epochs: 10
  num_gpus: 1
  num_nodes: 1
  optim:
    lr: 0.0002
    lr_backbone: 2.0e-05
    lr_steps:
    - 10
  precision: fp32
  validation_interval: 1
  val_interval: 1
  # verbose: true

Running the training from this container directly, no apt updates, pulled unmodified.
(Hydra) gives the following error:

UserWarning: 
'jupyter_cfg.yaml' is validated against ConfigStore schema with the same name.
This behavior is deprecated in Hydra 1.1 and will be removed in Hydra 1.2.
See https://hydra.cc/docs/next/upgrades/1.0_to_1.1/automatic_schema_matching for migration instructions.
  _run_hydra(
Error merging 'jupyter_cfg.yaml' with schema
Cannot merge DictConfig with ListConfig
    full_key: 
    object_type=ExperimentConfig

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