class_docstr: Torchscript-enabled version of preprocessing done by SetFeatureMixin.add_feature_data.
  
  If is_bag is true, forward returns a vector for each sample indicating counts of each token. Else, forward returns a
  multi-hot vector for each sample indicating presence of each token.
docstr: Takes a list of strings and returns a tensor of counts for each token.
docstr_tok: ["Takes","a","list","of","strings","and","returns","a","tensor","of","counts","for","each","token","."]
code: def forward(self, v: TorchscriptPreprocessingInput) -> torch.Tensor:
      """Takes a list of strings and returns a tensor of counts for each token."""
      if not torch.jit.isinstance(v, List[str]):
          raise ValueError(f"Unsupported input: {v}")
  
      if self.lowercase:
          sequences = [sequence.lower() for sequence in v]
      else:
          sequences = v
  
      unit_sequences = self.tokenizer(sequences)
      # refines type of unit_sequences from Any to List[List[str]]
      assert torch.jit.isinstance(
          unit_sequences, List[List[str]]
      ), "unit_sequences is not a list of lists."
  
      set_matrix = torch.zeros(
          len(unit_sequences), self.vocab_size, dtype=torch.float32
      )
      for sample_idx, unit_sequence in enumerate(unit_sequences):
          sequence_length = len(unit_sequence)
          for i in range(sequence_length):
              curr_unit = unit_sequence[i]
              if curr_unit in self.unit_to_id:
                  curr_id = self.unit_to_id[curr_unit]
              else:
                  curr_id = self.unit_to_id[self.unknown_symbol]
  
              if self.is_bag:
                  set_matrix[sample_idx][curr_id] += 1
              else:
                  set_matrix[sample_idx][curr_id] = 1
  
      return set_matrix
  
imports: import logging
  from typing import Any, Dict, List, Union
  import numpy
  import torch
  from ludwig.constants import COLUMN, HIDDEN, LOGITS, NAME, PREDICTIONS, PROBABILITIES, PROC_COLUMN, SET
  from ludwig.features.base_feature import BaseFeatureMixin, InputFeature, OutputFeature, PredictModule
  from ludwig.features.feature_utils import set_str_to_idx
  from ludwig.schema.features.set_feature import SetInputFeatureConfig, SetOutputFeatureConfig
  from ludwig.types import FeatureMetadataDict, FeaturePostProcessingOutputDict, ModelConfigDict, PreprocessingConfigDict, TrainingSetMetadataDict
  from ludwig.utils import output_feature_utils
  from ludwig.utils.strings_utils import create_vocabulary, UNKNOWN_SYMBOL
  from ludwig.utils.tokenizers import get_tokenizer_from_registry, TORCHSCRIPT_COMPATIBLE_TOKENIZERS
  from ludwig.utils.types import TorchscriptPreprocessingInput
label_desc: Use image node_id 2 for calling the _SetPreprocessing obj's underlying member method code with example usage: obj.forward(v) and returns: set_matrix