@chanis, Chenyang Liu, Corey Wharton, Daeyun Shin, Daniel Julius Lasiman, Daniel
Waterworth, Danijar Hafner, Darren Garvey, Denis Gorbachev, @DjangoPeng,
Egor-Krivov, Elia Palme, Eric Platon, Fabrizio Milo, Gaetan Semet, Georg
Nebehay, Gu Wang, Gustav Larsson, @haosdent, Harold Cooper, Hw-Zz, @ichuang,
Igor Babuschkin, Igor Macedo Quintanilha, Ilya Edrenkin, @ironhead, Jakub

  // Add inline pass.
  pass_manager.addPass(mlir::createInlinerPass());

  // Expands mhlo.tuple ops.
  pass_manager.addPass(
      mlir::mhlo::createExpandHloTuplesPass(entry_function_name.str()));
  // Flatten tuples for control flows.
  pass_manager.addNestedPass<mlir::func::FuncOp>(
      mlir::mhlo::createFlattenTuplePass());

  mlir::odml::AddMhloOptimizationPasses(
      pass_manager,

    Value n_plus_y = rewriter.create<AddOp>(loc, iotaN, y);

    // GatherOp is happy about letting us index out of bounds values, but those
    // values will be undefined. So we mask them later. Set up the boolean
    // expression that tells us which entries, in the output shape, are out of
    // bounds and thus become the padding_value.
    Value x_in_bounds = rewriter.create<AndOp>(
        loc,

Creates a dataset that applies `f` to the outputs of `input_dataset`.
  }];

  let description = [{
Unlike MapDataset, the `f` in FlatMapDataset is expected to return a
Dataset variant, and FlatMapDataset will flatten successive results
into a single Dataset.
  }];

  let arguments = (ins
    TF_VariantTensor:$input_dataset,
    Variadic<TF_Tensor>:$other_arguments,

    SymbolRefAttr:$f,