func_output_shape,
      mlir::cast<RankedTensorType>(input_.getType()).getElementType());

  auto tensor_cast = builder_.create<mlir::tensor::CastOp>(
      fused_func_op_.getLoc(), func_result_type, lstm_.getResult());
  builder_.create<mlir::func::ReturnOp>(fused_func_op_.getLoc(),
                                        tensor_cast.getResult());
  return success();
}

    }
    operands.push_back(val);
  }
  return builder->create<func::CallOp>(loc, fn, operands).getOperation();
}

// Prepares for jump to the given block by introducing necessary tensor_cast
// operations and returning Values of types required by the block.
//
// Requires the function to provide values for each of the block arguments and
// they should be pair-wise compatible for tensor cast.

      mlir::TF::RecvTPUEmbeddingActivationsOp::getOperationName(), context));
  supported_ops->insert(OperationName(
      mlir::TF::SendTPUEmbeddingGradientsOp::getOperationName(), context));
}

// Stack, TensorList and TensorArray ops are rewritten during the second phase
// of the bridge (compilation of TPUCompile op). They would not match any
// legalization/canonicalization pattern and have to be manually added to the

// Materialize the MlirPassthroughOp by replacing it with the MLIR module
// attached as an attribute.
std::unique_ptr<OperationPass<func::FuncOp>>
CreateMaterializePassthroughOpPass();

// Replicates the TensorList init op by undoing some CSE needed for correct
// shape assignment in shape_inference.
std::unique_ptr<OperationPass<func::FuncOp>>
CreateReplicateTensorListInitOpsPass();