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Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
target_xy_spacing_um: double
spacing_by_stage: struct<CS12: double, CS13: double, CS15: double, CS16: double, CS17: double, CS18: double, CS20: dou (... 32 chars omitted)
  child 0, CS12: double
  child 1, CS13: double
  child 2, CS15: double
  child 3, CS16: double
  child 4, CS17: double
  child 5, CS18: double
  child 6, CS20: double
  child 7, CS21: double
  child 8, CS23: double
class_specimen_coverage: struct<Liver: list<item: string>, Allantois: list<item: string>, Peritoneal_cavity: list<item: strin (... 284 chars omitted)
  child 0, Liver: list<item: string>
      child 0, item: string
  child 1, Allantois: list<item: string>
      child 0, item: string
  child 2, Peritoneal_cavity: list<item: string>
      child 0, item: string
  child 3, Pericardial_cavity: list<item: string>
      child 0, item: string
  child 4, Neural_tube: list<item: string>
      child 0, item: string
  child 5, Mesonephros: list<item: string>
      child 0, item: string
  child 6, Mesonephric_duct: list<item: string>
      child 0, item: string
  child 7, Respiratory_system: list<item: string>
      child 0, item: string
  child 8, Trigeminal_ganglion_V: list<item: string>
      child 0, item: string
  child 9, Facial_vestibulocochlear_ganglia: list<item: string>
      child 0, item: string
pooled_case_ids_by_class: struct<Liver: list<item: string>, Allantois: list<item: string>, Peritoneal_cavity: list<item: strin (... 284 chars omitted)
  child 0, Liver: list<item: string>
      child 0, it
...
d 7, Respiratory_system: int64
  child 8, Trigeminal_ganglion_V: int64
  child 9, Facial_vestibulocochlear_ganglia: int64
is_cascaded: string
dataloader_val.generator: string
num_epochs: string
oversample_foreground_percent: string
is_ddp: string
disable_checkpointing: string
num_val_iterations_per_epoch: string
save_every: string
dataloader_train: string
dataloader_train.transform: string
preprocessed_dataset_folder: string
dataloader_val: string
label_manager: string
inference_allowed_mirroring_axes: string
output_folder: string
folder_with_segs_from_previous_stage: string
network: string
fold: string
configuration_manager: string
hostname: string
lr_scheduler: string
grad_scaler: string
cudnn_version: int64
dataloader_val.transform: string
output_folder_base: string
dataset_json: string
probabilistic_oversampling: string
my_init_kwargs: string
torch_version: string
plans_manager: string
dataloader_train.num_processes: string
dataloader_val.generator.transforms: string
enable_deep_supervision: string
dataloader_val.num_processes: string
local_rank: string
num_input_channels: string
current_epoch: string
weight_decay: string
dataloader_train.generator: string
was_initialized: string
batch_size: string
optimizer: string
_best_ema: string
initial_lr: string
preprocessed_dataset_folder_base: string
loss: string
configuration_name: string
gpu_name: string
logger: string
num_iterations_per_epoch: string
device: string
log_file: string
dataloader_train.generator.transforms: string
to
{'_best_ema': Value('string'), 'batch_size': Value('string'), 'configuration_manager': Value('string'), 'configuration_name': Value('string'), 'cudnn_version': Value('int64'), 'current_epoch': Value('string'), 'dataloader_train': Value('string'), 'dataloader_train.generator': Value('string'), 'dataloader_train.generator.transforms': Value('string'), 'dataloader_train.num_processes': Value('string'), 'dataloader_train.transform': Value('string'), 'dataloader_val': Value('string'), 'dataloader_val.generator': Value('string'), 'dataloader_val.generator.transforms': Value('string'), 'dataloader_val.num_processes': Value('string'), 'dataloader_val.transform': Value('string'), 'dataset_json': Value('string'), 'device': Value('string'), 'disable_checkpointing': Value('string'), 'enable_deep_supervision': Value('string'), 'fold': Value('string'), 'folder_with_segs_from_previous_stage': Value('string'), 'gpu_name': Value('string'), 'grad_scaler': Value('string'), 'hostname': Value('string'), 'inference_allowed_mirroring_axes': Value('string'), 'initial_lr': Value('string'), 'is_cascaded': Value('string'), 'is_ddp': Value('string'), 'label_manager': Value('string'), 'local_rank': Value('string'), 'log_file': Value('string'), 'logger': Value('string'), 'loss': Value('string'), 'lr_scheduler': Value('string'), 'my_init_kwargs': Value('string'), 'network': Value('string'), 'num_epochs': Value('string'), 'num_input_channels': Value('string'), 'num_iterations_per_epoch': Value('string'), 'num_val_iterations_per_epoch': Value('string'), 'optimizer': Value('string'), 'output_folder': Value('string'), 'output_folder_base': Value('string'), 'oversample_foreground_percent': Value('string'), 'plans_manager': Value('string'), 'preprocessed_dataset_folder': Value('string'), 'preprocessed_dataset_folder_base': Value('string'), 'probabilistic_oversampling': Value('string'), 'save_every': Value('string'), 'torch_version': Value('string'), 'was_initialized': Value('string'), 'weight_decay': Value('string')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              target_xy_spacing_um: double
              spacing_by_stage: struct<CS12: double, CS13: double, CS15: double, CS16: double, CS17: double, CS18: double, CS20: dou (... 32 chars omitted)
                child 0, CS12: double
                child 1, CS13: double
                child 2, CS15: double
                child 3, CS16: double
                child 4, CS17: double
                child 5, CS18: double
                child 6, CS20: double
                child 7, CS21: double
                child 8, CS23: double
              class_specimen_coverage: struct<Liver: list<item: string>, Allantois: list<item: string>, Peritoneal_cavity: list<item: strin (... 284 chars omitted)
                child 0, Liver: list<item: string>
                    child 0, item: string
                child 1, Allantois: list<item: string>
                    child 0, item: string
                child 2, Peritoneal_cavity: list<item: string>
                    child 0, item: string
                child 3, Pericardial_cavity: list<item: string>
                    child 0, item: string
                child 4, Neural_tube: list<item: string>
                    child 0, item: string
                child 5, Mesonephros: list<item: string>
                    child 0, item: string
                child 6, Mesonephric_duct: list<item: string>
                    child 0, item: string
                child 7, Respiratory_system: list<item: string>
                    child 0, item: string
                child 8, Trigeminal_ganglion_V: list<item: string>
                    child 0, item: string
                child 9, Facial_vestibulocochlear_ganglia: list<item: string>
                    child 0, item: string
              pooled_case_ids_by_class: struct<Liver: list<item: string>, Allantois: list<item: string>, Peritoneal_cavity: list<item: strin (... 284 chars omitted)
                child 0, Liver: list<item: string>
                    child 0, it
              ...
              d 7, Respiratory_system: int64
                child 8, Trigeminal_ganglion_V: int64
                child 9, Facial_vestibulocochlear_ganglia: int64
              is_cascaded: string
              dataloader_val.generator: string
              num_epochs: string
              oversample_foreground_percent: string
              is_ddp: string
              disable_checkpointing: string
              num_val_iterations_per_epoch: string
              save_every: string
              dataloader_train: string
              dataloader_train.transform: string
              preprocessed_dataset_folder: string
              dataloader_val: string
              label_manager: string
              inference_allowed_mirroring_axes: string
              output_folder: string
              folder_with_segs_from_previous_stage: string
              network: string
              fold: string
              configuration_manager: string
              hostname: string
              lr_scheduler: string
              grad_scaler: string
              cudnn_version: int64
              dataloader_val.transform: string
              output_folder_base: string
              dataset_json: string
              probabilistic_oversampling: string
              my_init_kwargs: string
              torch_version: string
              plans_manager: string
              dataloader_train.num_processes: string
              dataloader_val.generator.transforms: string
              enable_deep_supervision: string
              dataloader_val.num_processes: string
              local_rank: string
              num_input_channels: string
              current_epoch: string
              weight_decay: string
              dataloader_train.generator: string
              was_initialized: string
              batch_size: string
              optimizer: string
              _best_ema: string
              initial_lr: string
              preprocessed_dataset_folder_base: string
              loss: string
              configuration_name: string
              gpu_name: string
              logger: string
              num_iterations_per_epoch: string
              device: string
              log_file: string
              dataloader_train.generator.transforms: string
              to
              {'_best_ema': Value('string'), 'batch_size': Value('string'), 'configuration_manager': Value('string'), 'configuration_name': Value('string'), 'cudnn_version': Value('int64'), 'current_epoch': Value('string'), 'dataloader_train': Value('string'), 'dataloader_train.generator': Value('string'), 'dataloader_train.generator.transforms': Value('string'), 'dataloader_train.num_processes': Value('string'), 'dataloader_train.transform': Value('string'), 'dataloader_val': Value('string'), 'dataloader_val.generator': Value('string'), 'dataloader_val.generator.transforms': Value('string'), 'dataloader_val.num_processes': Value('string'), 'dataloader_val.transform': Value('string'), 'dataset_json': Value('string'), 'device': Value('string'), 'disable_checkpointing': Value('string'), 'enable_deep_supervision': Value('string'), 'fold': Value('string'), 'folder_with_segs_from_previous_stage': Value('string'), 'gpu_name': Value('string'), 'grad_scaler': Value('string'), 'hostname': Value('string'), 'inference_allowed_mirroring_axes': Value('string'), 'initial_lr': Value('string'), 'is_cascaded': Value('string'), 'is_ddp': Value('string'), 'label_manager': Value('string'), 'local_rank': Value('string'), 'log_file': Value('string'), 'logger': Value('string'), 'loss': Value('string'), 'lr_scheduler': Value('string'), 'my_init_kwargs': Value('string'), 'network': Value('string'), 'num_epochs': Value('string'), 'num_input_channels': Value('string'), 'num_iterations_per_epoch': Value('string'), 'num_val_iterations_per_epoch': Value('string'), 'optimizer': Value('string'), 'output_folder': Value('string'), 'output_folder_base': Value('string'), 'oversample_foreground_percent': Value('string'), 'plans_manager': Value('string'), 'preprocessed_dataset_folder': Value('string'), 'preprocessed_dataset_folder_base': Value('string'), 'probabilistic_oversampling': Value('string'), 'save_every': Value('string'), 'torch_version': Value('string'), 'was_initialized': Value('string'), 'weight_decay': Value('string')}
              because column names don't match

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