code: def alt_to_interp_rep(
      curr_sample: TensorOrTupleOfTensorsGeneric,
      original_input: TensorOrTupleOfTensorsGeneric,
      **kwargs: Any,
  ) -> Tensor:
      binary_vector = torch.zeros(1, kwargs["num_interp_features"])
      feature_mask = kwargs["feature_mask"]
      for i in range(kwargs["num_interp_features"]):
          curr_total = 1
          if isinstance(curr_sample, Tensor):
              if (
                  torch.sum(
                      torch.abs(
                          (feature_mask == i).float()
                          * (curr_sample - original_input)
                      )
                  )
                  > 0.001
              ):
                  curr_total = 0
          else:
              sum_diff = sum(
                  torch.sum(
                      torch.abs((mask == i).float() * (sample - inp))
                  )
                  for inp, sample, mask in zip(
                      original_input, curr_sample, feature_mask
                  )
              )
              if sum_diff > 0.001:
                  curr_total = 0
          binary_vector[0][i] = curr_total
      return binary_vector
  
imports: import io
  import unittest
  import unittest.mock
  from functools import partial
  from typing import Any, Callable, Generator, List, Optional, Tuple, Union
  import torch
  from captum._utils.models.linear_model import SGDLasso, SkLearnLasso
  from captum._utils.models.model import Model
  from captum._utils.typing import BaselineType, TensorOrTupleOfTensorsGeneric
  from captum.attr._core.lime import get_exp_kernel_similarity_function, Lime, LimeBase
  from captum.attr._utils.batching import _batch_example_iterator
  from captum.attr._utils.common import _construct_default_feature_mask, _format_input_baseline, _format_tensor_into_tuples
  from tests.helpers.basic import assertTensorAlmostEqual, assertTensorTuplesAlmostEqual, BaseTest
  from tests.helpers.basic_models import BasicLinearModel, BasicModel_MultiLayer, BasicModel_MultiLayer_MultiInput, BasicModelBoolInput
  from torch import Tensor
label_desc: Use image node_id 3 for calling a global function with example usage: alt_to_interp_rep(curr_sample, original_input) and returns: binary_vector