// Guarantee all functions have one use, which enables more exact shape
  // inference.
  pm.addPass(mlir::TF::CreateGuaranteeAllFuncsOneUsePass());
  // Run shape inference so that tf_executor/tf_device ops created later will
  // likely to inherit more concrete types.
  pm.addPass(mlir::TF::CreateTFShapeInferencePass());
  pm.addNestedPass<FuncOp>(mlir::TFTPU::CreateTPUPartitionedOpConversionPass());
  pm.addNestedPass<FuncOp>(

    // Returning null tensors from a tf.function is not supported, so we keep
    // track of indices in the model's outputs are nullptr in this set.
    // The FunctionDef only outputs the non-null tensors. We later pad the
    // function op outputs to have nullptrs at the `null_indices`.
    absl::flat_hash_set<int> null_indices;
    {
      AbstractContextPtr func_ctx(BuildFunction(fn_name));

// Node_Y will receive both scopes "unstage" and "stage", while Node_X receives
// only scope "stage".  The semantic of scope "unstage" is preserved although
// scope "stage" is later appended.  As a result, Node_X and Node_Y will be put
// into different clusters.
//
//                Unstage -> Node_Y (scope "unstage & stage")
//                              |
//                              V

    // Generate a Max and an ArgMax of as the mhlo op returns both while in TF
    // we have separate ops for them. If only one of them is used then the other
    // one will be garbage collected later.
    if (!mlir::isa<ShapedType>(operand.getType())) return failure();
    auto operand_type = mlir::cast<ShapedType>(operand.getType());
    if (operand_type.getElementType().isInteger(1)) {

    return false;
  }

  // If any of the result types are variants, don't try to constant fold them.
  // This creates opaque variant constants which lose information and would
  // require "raising" later.
  for (const Type type : inst->getResultTypes()) {
    if (const TensorType tensor_type = mlir::dyn_cast<TensorType>(type)) {
      if (mlir::isa<VariantType>(tensor_type.getElementType())) {
        return false;
      }

// with appropriate shape to match with the shape of XlaDotV2 result.
// We didn't apply XlaEinsum or XlaDotV2 for this work, since it would loose
// the chance for constant folding later. We could try to add some
// postprocessing passes later to further optimize the graph after constant
// folding.
Value CreateZeroPointPartialOffsetXlaDotV2(
    OpBuilder &builder, Location loc, Value tensor,

    if (stateless && stateless.getValue())
      *node_def->mutable_op() = "Stateless" + node_def->op();
  }

  // Add inputs to the NodeDef based on the number of operands. This is required
  // as later when edges are added to the Node using Graph::AddEdge the
  // associated NodeDef is not updated.
  for (int i = 0, e = inst->getNumOperands(); i < e; ++i) {
    node_def->add_input();
  }

    // because a race condition can happen between this and another thread that
    // turns off some variable's `copy_on_read_mode` after this thread enables
    // sparse access; when a later function sees `copy_on_read_mode` is off, it
    // will try to lock the variable again for updating `copy_on_read_mode` and
    // cause the deadlock, since the variable mutex is non-re-entrant.
    for (auto* var : vars) {

    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,