docstr: Returns Q-values, 1 for each candidate document, given user and doc tensors.
  
  Args:
      user: [B x u] where u=embedding of user features.
      docs: List[[B x d]] where d=embedding of doc features. Each item in the
          list represents one document candidate.
  
  Returns:
      Tensor ([batch, num candidates) of Q-values.
      1 Q-value per document candidate.
docstr_tok: ["Returns","Q-values",",","1","for","each","candidate","document",",","given","user","and","doc","tensors",".","Args",":","user",":","[","B","x","u","]","where","u=embedding","of","user","features",".","docs",":","List","[","[","B","x","d","]","]","where","d=embedding","of","doc","features",".","Each","item","in","the","list","represents","one","document","candidate",".","Returns",":","Tensor","(","[","batch",",","num","candidates",")","of","Q-values",".","1","Q-value","per","document","candidate","."]
code: def forward(
      self, user: TensorType, docs: List[TensorType]
  ) -> TensorType:
      """Returns Q-values, 1 for each candidate document, given user and doc tensors.
  
      Args:
          user: [B x u] where u=embedding of user features.
          docs: List[[B x d]] where d=embedding of doc features. Each item in the
              list represents one document candidate.
  
      Returns:
          Tensor ([batch, num candidates) of Q-values.
          1 Q-value per document candidate.
      """
      q_outs = []
      for i in range(self.num_candidates):
          user_cat_doc = torch.cat([user, docs[i]], dim=1)
          q_outs.append(self.q_nets[i](user_cat_doc))
  
      return torch.cat(q_outs, dim=1)
  
file_path: ray/rllib_contrib/slate_q/src/rllib_slate_q/slate_q/slateq_torch_model.py
imports: from typing import List, Sequence
  import gymnasium
  from ray.rllib.models.torch.misc import SlimFC
  from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.typing import ModelConfigDict, TensorType
label_desc: Use image node_id 2 for calling the QValueModel obj's underlying member method code with example usage: obj.forward(user, docs) and returns: torch