code: def forward(self, inputs: torch.Tensor, mask=None):
      assert isinstance(inputs, torch.Tensor)
      assert inputs.dtype in [
          torch.int8,
          inputs.dtype,
          torch.int16,
          torch.int32,
          torch.int64,
      ]
      assert len(inputs.shape) == 2
      inputs_exp = inputs.type(torch.int32)
      inputs_mask = torch.not_equal(inputs, SpecialSymbol.PADDING.value)
      lengths = torch.sum(inputs_mask.type(torch.int32), dim=1)
      encoder_output = self.encoder_obj(inputs_exp, mask=inputs_mask)
      encoder_output[LENGTHS] = lengths
      return encoder_output
  
imports: import logging
  from functools import partial
  from typing import Dict, List, Union
  import numpy
  import torch
  from ludwig.constants import COLUMN, LAST_PREDICTIONS, LENGTHS, NAME, PREDICTIONS, PROBABILITIES, PROBABILITY, PROC_COLUMN, SEQUENCE
  from ludwig.features.base_feature import BaseFeatureMixin, InputFeature, OutputFeature, PredictModule
  from ludwig.features.feature_utils import compute_sequence_probability, compute_token_probabilities
  from ludwig.schema.features.sequence_feature import SequenceInputFeatureConfig, SequenceOutputFeatureConfig
  from ludwig.types import FeatureMetadataDict, FeaturePostProcessingOutputDict, ModelConfigDict, PreprocessingConfigDict, TrainingSetMetadataDict
  from ludwig.utils import output_feature_utils
  from ludwig.utils.math_utils import softmax
  from ludwig.utils.strings_utils import build_sequence_matrix, create_vocabulary, SpecialSymbol, START_SYMBOL, STOP_SYMBOL, UNKNOWN_SYMBOL
  from ludwig.utils.tokenizers import get_tokenizer_from_registry
  from ludwig.utils.types import TorchscriptPreprocessingInput
label_desc: Use image node_id 2 for calling the SequenceInputFeature obj's underlying member method code with example usage: obj.forward(inputs, mask) and returns: encoder_output