// {"Flags":0,"ParamFlags":[32],"ResultFlags":null}
// callsite: acrosscall.go:58:9|0 flagstr "" flagval 0 score 8 mask 0 maskstr ""
// <endcallsites>
// <endfuncpreamble>
func T_feeds_if_via_call(x int) {
	feedsif(x)
}

// acrosscall.go T_feeds_if_via_call_conditional 69 0 1
// ParamFlags
//   0 ParamMayFeedIfOrSwitch
// <endpropsdump>
// {"Flags":0,"ParamFlags":[64],"ResultFlags":null}

	// Parameter value feeds unmodified into a top level indirect
	// function call (assumes parameter is of function type).
	ParamFeedsIndirectCall

	// Parameter value feeds unmodified into an indirect function call
	// that is conditional/nested (not guaranteed to execute). Assumes
	// parameter is of function type.
	ParamMayFeedIndirectCall

	// Parameter value feeds unmodified into a top level "switch"

                          std::vector<Tensor>* outputs,
                          RunMetadata* run_metadata) const {
  std::vector<std::pair<string, Tensor>> feeds;
  feeds.reserve(inputs.size());
  for (auto const& feed : inputs) {
    TF_RETURN_IF_ERROR(feed.second.status);
    feeds.emplace_back(std::piecewise_construct,
                       std::forward_as_tuple(feed.first.name()),

    ClientSession session(scope_);

    DCHECK_EQ(input_values.size(), inputs_.size());
    ClientSession::FeedType feeds;
    for (int i = 0; i < inputs_.size(); ++i) {
      feeds.emplace(inputs_[i], input_values[i]);
    }

    std::vector<Tensor> out_tensors;
    TF_ASSERT_OK(session.Run(feeds, outputs_, &out_tensors));
    ASSERT_EQ(out_tensors.size(), outputs_.size());

///     std::vector<Tensor> outputs;
///
///     Status s = session.Run({ {a, {1}} }, {c}, &outputs);
///     if (!s.ok()) { ... }
class ClientSession {
 public:
  /// A data type to represent feeds to a Run call.
  ///
  /// This is a map of `Output` objects returned by op-constructors to the value
  /// to feed them with. See `Input::Initializer` for details on what can be
  /// used as feed values.

// <endcallsites>
// <endfuncpreamble>
func T_chained_indirect_call(x, y int) {
	// Here 'returnsFunc' returns an inlinable func that feeds
	// directly into a call (no named intermediate).
	G += returnsFunc(x - y)(x + y)
}

// returns2.go T_chained_conc_iface_call 144 0 1
// <endpropsdump>
// {"Flags":0,"ParamFlags":[0,0],"ResultFlags":null}

  func::FuncOp func = getOperation();
  // Convert all the DeviceIndex ops to constant values.
  func.getBody().walk([](TF::DeviceIndexOp op) {
    // This just selects the default in all cases where DeviceIndex feeds into
    // tf.Case. This could be enhanced to have some sort of policy in the
    // future.
    OpBuilder b(op);
    RankedTensorType type = RankedTensorType::get({}, b.getIntegerType(32));

  }

  // Don't prune if the condition block argument has any user.
  if (!cond_block_argument.use_empty()) return false;

  // Don't prune if `body_yield_operand` has more than one use (that would mean
  // it feeds into another op apart from `Yield`).
  if (!body_yield_operand.hasOneUse()) return false;

  // Don't prune if any other result of the candidate op is used.
  for (Value candidate_result : candidate_op->getResults()) {

    }
    i += Character.charCount(codePoint)
  }
}

/**
 * Returns a substring of `input` on the range `[pos..limit)` with the following
 * transformations:
 *
 *  * Tabs, newlines, form feeds and carriage returns are skipped.
 *
 *  * In queries, ' ' is encoded to '+' and '+' is encoded to "%2B".
 *
 *  * Characters in `encodeSet` are percent-encoded.
 *

  llvm::SmallDenseSet<mlir::StringAttr, 4> ops_to_preserve_ids_;
};

// Checks if a tf_executor.Graph can be pruned.
// For TensorFlow V1.0 compatibility: when importing a graph without providing
// feeds/fetches/targets we should not attempt to prune. The best approximation
// here is to check if the graph is of the "main" function and does not have the
// "tf.entry_function" attribute defined.
bool CanPruneGraph(func::FuncOp func) {