};

// Extracts inner ops of tf_executor.island ops in a tf_executor.graph, in the
// order of ops in tf_executor.graph.
LogicalResult LiftIslandOpInnerOpsFromGraph(tf_executor::GraphOp graph) {
  auto graph_position = graph.getOperation()->getIterator();
  Block* parent_block = graph.getOperation()->getBlock();
  for (Operation& op : graph.GetBody().without_terminator()) {
    auto island_op = llvm::dyn_cast<tf_executor::IslandOp>(op);

      std::next(copy_range.begin()) == copy_range.end() &&
      isa<tf_executor::GraphOp>(*copy_range.begin())) {
    // Already a graph.
    return;
  }

  auto return_op = dyn_cast<func::ReturnOp>(body.getTerminator());
  if (!return_op) {
    LLVM_DEBUG(llvm::dbgs() << "Expect function to end with return\n");
    return;
  }
  // Build GraphOp.
  OpBuilder builder(&body, body.begin());

			return nil
		}
	}
}

func (d *decoder) readGraphicControl() error {
	if err := readFull(d.r, d.tmp[:6]); err != nil {
		return fmt.Errorf("gif: can't read graphic control: %s", err)
	}
	if d.tmp[0] != 4 {
		return fmt.Errorf("gif: invalid graphic control extension block size: %d", d.tmp[0])
	}
	flags := d.tmp[1]
	d.disposalMethod = (flags & gcDisposalMethodMask) >> 2
	d.delayTime = int(d.tmp[2]) | int(d.tmp[3])<<8

awaiting_label = "awaiting-review"
lang_all_label = "lang-all"
approved_label = "approved-2"
translations_path = Path(__file__).parent / "translations.yml"

github_graphql_url = "https://api.github.com/graphql"
questions_translations_category_id = "DIC_kwDOCZduT84CT5P9"

all_discussions_query = """
query Q($category_id: ID) {
  repository(name: "fastapi", owner: "tiangolo") {

#include "tensorflow/core/framework/node_def_util.h"
#include "tensorflow/core/framework/tensor.pb.h"
#include "tensorflow/core/graph/algorithm.h"
#include "tensorflow/core/graph/control_flow.h"
#include "tensorflow/core/graph/graph.h"
#include "tensorflow/core/graph/graph_def_builder.h"
#include "tensorflow/core/graph/tensor_id.h"
#include "tensorflow/core/lib/gtl/map_util.h"
#include "tensorflow/core/lib/hash/hash.h"

// the fetch values in the main GraphOp corresponding to the original fetch
// values from `src_func_op`. Returns an empty vector when `src_func_op` is
// empty. `main_func_op` must have a GraphOp.
SmallVector<Value> CopyOpsToMainFunction(func::FuncOp src_func_op,
                                         func::FuncOp main_func_op) {
  GraphOp src_graph_op = GetGraphOpFromFuncOp(src_func_op);
  if (!src_graph_op) {

    // The pass runs on a valid tf_executor dialect, so the op should be the
    // GraphOp.
    return cast<GraphOp>(graph_op_range.begin());
  }

  return {};
}

void MergeDuplicateResourceOpsPass::runOnOperation() {
  func::FuncOp func_op = getOperation();
  GraphOp graph_op = GetGraphOpFromFuncOp(func_op);
  if (!graph_op) return;

  llvm::StringMap<Operation*> shared_name_to_resource;

It's a common misconception that there's a single dependency graph for an application.
In fact Gradle will, during a build, resolve a number of distinct dependency graphs, even within a single project.
For example, the graph of dependencies to use at compile time is different from the graph of dependencies to use at runtime.

# RUN: not tf-mlir-translate -graphdef-to-mlir -tf-enable-shape-inference-on-import=false %s -o - 2>&1 | FileCheck %s

this is not a valid graph def

# CHECK: Error parsing Protobuf

The region attached to `tf_executor.graph` is terminated by a
`tf_executor.fetch` operation. The non-control operands of the terminator
correspond to the result values (or fetches) of the `tf_executor.graph`
operation. The behavior is undefined if any of the operands of the
`tf_executor.fetch` is dead.

```mlir {.mlir}
%fetches = tf_executor.graph : tensor<*xf32> {