docstr: Params:
      inputs: The inputs fed into the encoder.
              Shape: [batch x vocab_size], type tf.int32.
  
  Returns:
      Embeddings of shape [batch x vocab_size x embed size], type float32.
docstr_tok: ["Params",":","inputs",":","The","inputs","fed","into","the","encoder",".","Shape",":","[","batch","x","vocab_size","]",",","type","tf.int32",".","Returns",":","Embeddings","of","shape","[","batch","x","vocab_size","x","embed","size","]",",","type","float32","."]
code: def forward(self, inputs: torch.Tensor) -> EncoderOutputDict:
      """
      Params:
          inputs: The inputs fed into the encoder.
                  Shape: [batch x vocab_size], type tf.int32.
  
      Returns:
          Embeddings of shape [batch x vocab_size x embed size], type float32.
      """
      hidden = self.embed(inputs)
      hidden = self.fc_stack(hidden)
  
      return {ENCODER_OUTPUT: hidden}
  
file_path: ludwig/ludwig/encoders/set_encoders.py
imports: import logging
  from typing import Any, Dict, List, Optional, Type
  import torch
  from ludwig.api_annotations import DeveloperAPI
  from ludwig.constants import ENCODER_OUTPUT, SET
  from ludwig.encoders.base import Encoder
  from ludwig.encoders.registry import register_encoder
  from ludwig.encoders.types import EncoderOutputDict
  from ludwig.modules.embedding_modules import EmbedSet
  from ludwig.modules.fully_connected_modules import FCStack
  from ludwig.schema.encoders.base import BaseEncoderConfig
  from ludwig.schema.encoders.set_encoders import SetSparseEncoderConfig
label_desc: Use image node_id 2 for calling the SetSparseEncoder obj's underlying member method code with example usage: obj.forward(inputs) and returns: dict