code: def forward(self, inputs, mask=None):
      assert isinstance(inputs, torch.Tensor)
      assert inputs.dtype in [
          torch.float16,
          torch.float32,
          torch.float64,
      ]
      assert len(inputs.shape) == 2
  
      inputs_exp = inputs.type(torch.float32)
      encoder_output = self.encoder_obj(inputs_exp, mask=mask)
  
      return encoder_output
  
imports: import logging
  from typing import Dict, List, TYPE_CHECKING, Union
  import numpy
  import torch
  from ludwig.constants import COLUMN, HIDDEN, LOGITS, NAME, PREDICTIONS, PROC_COLUMN, TIMESERIES
  from ludwig.features.base_feature import BaseFeatureMixin, OutputFeature, PredictModule
  from ludwig.features.sequence_feature import SequenceInputFeature
  from ludwig.features.vector_feature import _VectorPostprocessing, _VectorPredict
  from ludwig.schema.features.timeseries_feature import TimeseriesInputFeatureConfig, TimeseriesOutputFeatureConfig
  from ludwig.types import FeatureMetadataDict, ModelConfigDict, PreprocessingConfigDict, TrainingSetMetadataDict
  from ludwig.utils.tokenizers import get_tokenizer_from_registry, TORCHSCRIPT_COMPATIBLE_TOKENIZERS
  from ludwig.utils.types import Series, TorchscriptPreprocessingInput
label_desc: Use image node_id 2 for calling the TimeseriesInputFeature obj's underlying member method code with example usage: obj.forward(inputs, mask) and returns: encoder_output