class_docstr: Base model class which inherits from nn.Module. Also has a stage flag to
  indicate it's in `train`, `eval`, or `test` stage.
  This is because the built-in train/eval flag in PyTorch can't distinguish eval
  and test, which is required to support some use cases.
class_docstr_tok: ["Base","model","class","which","inherits","from","nn.Module",".","Also","has","a","stage","flag","to","indicate","it","'s","in","`","train","`",",","`","eval","`",",","or","`","test","`","stage",".","This","is","because","the","built-in","train\/eval","flag","in","PyTorch","ca","n't","distinguish","eval","and","test",",","which","is","required","to","support","some","use","cases","."]
code: def train_batch(cls, model, batch, state=None):
      # This is a class method so that it works when model is a DistributedModel
      # wrapper. Otherwise the forward call here skips the DDP forward call.
  
      # Forward pass through the network.
      model_inputs = model.arrange_model_inputs(batch)
      model_context = model.arrange_model_context(batch)
      targets = model.arrange_targets(batch)
      model_outputs = model(*model_inputs)
  
      # Add stage to context.
      if state:
          if model_context is None:
              model_context = {"stage": state.stage}
          else:
              model_context["stage"] = state.stage
  
      # Compute loss and predictions.
      loss = maybe_float(
          model.get_loss(model_outputs, targets, model_context)
      )
      predictions, scores = model.get_pred(
          model_outputs, context=model_context
      )
  
      # Pack results and return them.
      metric_data = (predictions, targets, scores, loss, model_inputs)
      return loss, metric_data
  
imports: import os
  from typing import Dict, List, Union
  import torch
  import torch.jit
  import torch.nn
  from pytext.common.constants import Stage, TORCH_VERSION
  from pytext.config.component import Component, ComponentType
  from pytext.config.doc_classification import ModelInput
  from pytext.config.field_config import FeatureConfig
  from pytext.config.pytext_config import ConfigBase, ConfigBaseMeta
  from pytext.data import CommonMetadata
  from pytext.data.tensorizers import Tensorizer
  from pytext.models.module import create_module
  from pytext.utils.file_io import PathManager
  from pytext.utils.path import is_absolute_path
  from pytext.utils.precision import maybe_float
  from pytext.utils.usage import log_class_usage
  from .decoders import DecoderBase
  from .embeddings import EmbeddingBase, EmbeddingList
  from .output_layers import OutputLayerBase
  from .representations.representation_base import RepresentationBase
label_desc: Use image node_id 10 for calling the BaseModel obj's underlying member method code with example usage: obj.train_batch(cls, model, batch, state) and returns: loss,  metric_data