class_docstr: Train Task for the TwoTower model. Adds BinaryCrossEntropy Loss.  to use with train_pipeline
  
  Args:
      two_tower (TwoTower): two tower model
  
  Example::
  
      m = TwoTowerTrainTask(two_tower_model)
docstr: Args:
      batch (Batch): batch from torchrec.datasets
  
  Returns:
      Tuple[loss, Tuple[loss, logits, labels]]: each of shape B x 1
docstr_tok: ["Args",":","batch","(","Batch",")",":","batch","from","torchrec.datasets","Returns",":","Tuple","[","loss",",","Tuple","[","loss",",","logits",",","labels","]","]",":","each","of","shape","B","x","1"]
code: def forward(
      self, batch: Batch
  ) -> Tuple[
      torch.Tensor, Tuple[torch.Tensor, torch.Tensor, torch.Tensor]
  ]:
      """
      Args:
          batch (Batch): batch from torchrec.datasets
  
      Returns:
          Tuple[loss, Tuple[loss, logits, labels]]: each of shape B x 1
      """
      query_embedding, candidate_embedding = self.two_tower(
          batch.sparse_features
      )
      logits = (
          (query_embedding * candidate_embedding).sum(dim=1).squeeze()
      )
      loss = self.loss_fn(logits, batch.labels.float())
      return loss, (
          loss.detach(),
          logits.detach(),
          batch.labels.detach(),
      )
  
imports: from typing import Any, List, Mapping, Optional, OrderedDict, Tuple, Union
  import faiss
  import faiss.contrib.torch_utils
  import torch
  from torch import nn
  from torchrec.datasets.utils import Batch
  from torchrec.modules.embedding_modules import EmbeddingBagCollection
  from torchrec.modules.mlp import MLP
  from torchrec.sparse.jagged_tensor import KeyedJaggedTensor
label_desc: Use image node_id 2 for calling the TwoTowerTrainTask obj's underlying member method code with example usage: obj.forward(batch) and returns: loss,  loss,  logits,  batch