docstr: :param inputs: The inputs fed into the encoder.
         Shape: [batch x 1], type tf.float32
docstr_tok: [":","param","inputs",":","The","inputs","fed","into","the","encoder",".","Shape",":","[","batch","x","1","]",",","type","tf.float32"]
code: def forward(
      self, inputs: torch.Tensor, mask: Optional[torch.Tensor] = None
  ) -> EncoderOutputDict:
      """
      :param inputs: The inputs fed into the encoder.
             Shape: [batch x 1], type tf.float32
      """
      return {ENCODER_OUTPUT: inputs}
  
imports: import logging
  from typing import Optional, Type
  import torch
  from ludwig.api_annotations import DeveloperAPI
  from ludwig.constants import BINARY, ENCODER_OUTPUT, NUMBER, TEXT, TIMESERIES, VECTOR
  from ludwig.encoders.base import Encoder
  from ludwig.encoders.registry import register_encoder
  from ludwig.encoders.types import EncoderOutputDict
  from ludwig.modules.fully_connected_modules import FCStack
  from ludwig.schema.encoders.base import BaseEncoderConfig, DenseEncoderConfig, PassthroughEncoderConfig
label_desc: Use image node_id 2 for calling the PassthroughEncoder obj's underlying member method code with example usage: obj.forward(inputs, mask) and returns: dict