code: def create_generic_benchmark_from_tensor_lists(
      train_tensors: Sequence[Sequence[Any]],
      test_tensors: Sequence[Sequence[Any]],
      *,
      other_streams_tensors: Optional[
          Dict[str, Sequence[Sequence[Any]]]
      ] = None,
      task_labels: Sequence[int],
      complete_test_set_only: bool = False,
      train_transform=None,
      train_target_transform=None,
      eval_transform=None,
      eval_target_transform=None,
      other_streams_transforms: Optional[
          Dict[str, Tuple[Any, Any]]
      ] = None,
  ) -> GenericCLScenario:
      """
      Creates a benchmark instance given lists of Tensors. A separate dataset will
      be created from each Tensor tuple (x, y, z, ...) and each of those training
      datasets will be considered a separate training experience. Using this
      helper function is the lowest-level way to create a Continual Learning
      benchmark. When possible, consider using higher level helpers.
  
      Experiences are defined by passing lists of tensors as the `train_tensors`,
      `test_tensors` (and `other_streams_tensors`) parameters. Those parameters
      must be lists containing lists of tensors, one list for each experience.
      Each tensor defines the value of a feature ("x", "y", "z", ...) for all
      patterns of that experience.
  
      By default the second tensor of each experience will be used to fill the
      `targets` value (label of each pattern).
  
      Beware that task labels can only be defined by choosing a single task label
      for each experience (the same task label is applied to all patterns of
      experiences sharing the same position in different streams).
  
      When in need to create a benchmark instance in which task labels are defined
      in a more fine-grained way, then consider using
      :func:`create_multi_dataset_generic_benchmark` by passing properly
      initialized :class:`AvalancheDataset` instances.
  
      :param train_tensors: A list of lists. The first list must contain the
          tensors for the first training experience (one tensor per feature), the
          second list must contain the tensors for the second training experience,
          and so on.
      :param test_tensors: A list of lists. The first list must contain the
          tensors for the first test experience (one tensor per feature), the
          second list must contain the tensors for the second test experience,
          and so on. When using `complete_test_set_only`, this parameter
          must be a list containing a single sub-list for the single test
          experience.
      :param other_streams_tensors: A dictionary describing the content of
          custom streams. Keys must be valid stream names (letters and numbers,
          not starting with a number) while the value follow the same structure
          of `train_tensors` and `test_tensors` parameters. If this
          dictionary contains the definition for "train" or "test" streams then
          those definition will  override the `train_tensors` and `test_tensors`
          parameters.
      :param task_labels: A list of task labels. Must contain at least a value
          for each experience. Each value describes the task label that will be
          applied to all patterns of a certain experience. For more info on that,
          see the function description.
      :param complete_test_set_only: If True, only the complete test set will
          be returned by the benchmark. This means that ``test_tensors`` must
          define a single experience. Defaults to False.
      :param train_transform: The transformation to apply to the training data,
          e.g. a random crop, a normalization or a concatenation of different
          transformations (see torchvision.transform documentation for a
          comprehensive list of possible transformations). Defaults to None.
      :param train_target_transform: The transformation to apply to training
          patterns targets. Defaults to None.
      :param eval_transform: The transformation to apply to the test data,
          e.g. a random crop, a normalization or a concatenation of different
          transformations (see torchvision.transform documentation for a
          comprehensive list of possible transformations). Defaults to None.
      :param eval_target_transform: The transformation to apply to test
          patterns targets. Defaults to None.
      :param other_streams_transforms: Transformations to apply to custom
          streams. If no transformations are defined for a custom stream,
          then "train" transformations will be used. This parameter must be a
          dictionary mapping stream names to transformations. The transformations
          must be a two elements tuple where the first element defines the
          X transformation while the second element is the Y transformation.
          Those elements can be None. If this dictionary contains the
          transformations for "train" or "test" streams then those transformations
          will override the `train_transform`, `train_target_transform`,
          `eval_transform` and `eval_target_transform` parameters.
  
      :returns: A :class:`GenericCLScenario` instance.
      """
  
      input_streams = dict(train=train_tensors, test=test_tensors)
  
      if other_streams_tensors is not None:
          input_streams = {**input_streams, **other_streams_tensors}
  
      stream_definitions: Dict[
          str, Sequence[TaskAwareClassificationDataset]
      ] = dict()
  
      for stream_name, list_of_exps_tensors in input_streams.items():
          stream_datasets: List[TaskAwareClassificationDataset] = []
          for exp_id, exp_tensors in enumerate(list_of_exps_tensors):
              stream_datasets.append(
                  _make_taskaware_tensor_classification_dataset(
                      *exp_tensors, task_labels=task_labels[exp_id]
                  )
              )
  
          stream_definitions[stream_name] = stream_datasets
  
      return create_multi_dataset_generic_benchmark(
          [],
          [],
          other_streams_datasets=stream_definitions,
          train_transform=train_transform,
          train_target_transform=train_target_transform,
          eval_transform=eval_transform,
          eval_target_transform=eval_target_transform,
          complete_test_set_only=complete_test_set_only,
          other_streams_transforms=other_streams_transforms,
      )
  
file_path: avalanche/avalanche/benchmarks/scenarios/deprecated/generic_benchmark_creation.py
imports: from pathlib import Path
  from typing import Generator, List, Mapping, Sequence, Union, Any, Tuple, Dict, Optional, Iterable, NamedTuple
  from avalanche.benchmarks.utils.classification_dataset import _make_taskaware_tensor_classification_dataset, _make_taskaware_classification_dataset
  from avalanche.benchmarks.utils import SupportedDataset, FilelistDataset, PathsDataset, common_paths_root
  from avalanche.benchmarks.utils.classification_dataset import TaskAwareClassificationDataset
  from avalanche.benchmarks.scenarios.deprecated.classification_scenario import GenericCLScenario
label_desc: Use image node_id 6 for calling a global function with example usage: create_generic_benchmark_from_tensor_lists(train_tensors, test_tensors) and returns: create_multi_dataset_generic_benchmark