code: def forward(
      model: Model[InT, InT], X: InT, is_train: bool
  ) -> Tuple[InT, Callable]:
      def backprop(d_output: InT) -> InT:
          dX = backprop_layer(d_output)
          if isinstance(d_output, list):
              return [d_output[i] + dX[i] for i in range(len(d_output))]
          elif isinstance(d_output, Ragged):
              return Ragged(d_output.data + dX.data, dX.lengths)
          elif isinstance(X, Padded):
              dX.data += d_output.data
              return dX
          else:
              return d_output + dX
  
      Y, backprop_layer = model.layers[0](X, is_train)
      if isinstance(X, list):
          return [X[i] + Y[i] for i in range(len(X))], backprop
      elif isinstance(X, Ragged):
          return Ragged(X.data + Y.data, X.lengths), backprop
      elif isinstance(X, Padded):
          Y.data += X.data
          return Y, backprop
      else:
          return X + Y, backprop
  
imports: from typing import Callable, List, Optional, Tuple, TypeVar
  from ..config import registry
  from ..model import Model
  from ..types import Floats1d, Floats2d, Floats3d, Floats4d, FloatsXd, Padded, Ragged
label_desc: Use image node_id 2 for calling a global function with example usage: forward(model, X, is_train) and returns: unknown,  backprop, unknown, Ragged,  backprop, Ragged, Y,  backprop, unknown,  backprop, dX, unknown