code: def default_from_interp_rep_transform(
      curr_sample, original_inputs, **kwargs
  ):
      assert (
          "feature_mask" in kwargs
      ), "Must provide feature_mask to use default interpretable representation transform"
      assert (
          "baselines" in kwargs
      ), "Must provide baselines to use default interpretable representation transform"
      feature_mask = kwargs["feature_mask"]
      if isinstance(feature_mask, Tensor):
          binary_mask = curr_sample[0][feature_mask].bool()
          return (
              binary_mask.to(original_inputs.dtype) * original_inputs
              + (~binary_mask).to(original_inputs.dtype)
              * kwargs["baselines"]
          )
      else:
          binary_mask = tuple(
              curr_sample[0][feature_mask[j]].bool()
              for j in range(len(feature_mask))
          )
          return tuple(
              binary_mask[j].to(original_inputs[j].dtype)
              * original_inputs[j]
              + (~binary_mask[j]).to(original_inputs[j].dtype)
              * kwargs["baselines"][j]
              for j in range(len(feature_mask))
          )
  
imports: import inspect
  import math
  import typing
  import warnings
  from typing import Any, Callable, cast, List, Optional, Tuple, Union
  import torch
  from captum._utils.common import _expand_additional_forward_args, _expand_target, _flatten_tensor_or_tuple, _format_output, _format_tensor_into_tuples, _get_max_feature_index, _is_tuple, _reduce_list, _run_forward
  from captum._utils.models.linear_model import SkLearnLasso
  from captum._utils.models.model import Model
  from captum._utils.progress import progress
  from captum._utils.typing import BaselineType, Literal, TargetType, TensorOrTupleOfTensorsGeneric
  from captum.attr._utils.attribution import PerturbationAttribution
  from captum.attr._utils.batching import _batch_example_iterator
  from captum.attr._utils.common import _construct_default_feature_mask, _format_input_baseline
  from captum.log import log_usage
  from torch import Tensor
  from torch.nn import CosineSimilarity
  from torch.utils.data import DataLoader, TensorDataset
label_desc: Use image node_id 1 for calling a global function with example usage: default_from_interp_rep_transform(curr_sample, original_inputs) and returns: unknown, tuple