#include "tensorflow/core/framework/tensor_testutil.h"
#include "tensorflow/core/graph/graph.h"
#include "tensorflow/core/graph/node_builder.h"
#include "tensorflow/core/platform/enable_tf2_utils.h"
#include "tensorflow/core/platform/types.h"
#include "tsl/lib/core/status_test_util.h"

namespace tensorflow {

namespace {

// Produce a valid graph with a resource-type input.
FunctionDef PassThroughResource() {

  auto f = ops::Neg(root.WithOpName("F"), e);
  auto g = ops::AddN(root.WithOpName("G"), std::initializer_list<Output>{e, f});

  std::unique_ptr<Graph> graph(new Graph(OpRegistry::Global()));
  TF_CHECK_OK(root.ToGraph(graph.get()));

  GraphShapeInfo shape_info;
  TF_ASSERT_OK(InferShapes(graph.get(), /*arg_shapes=*/{},
                           /*fnlib_def=*/nullptr, &shape_info));

  TF_Graph* const graph = s->graph;
  if (graph != nullptr) {
    graph->mu.lock();
    graph->sessions.erase(s);
    const bool del = graph->delete_requested && graph->sessions.empty();
    graph->mu.unlock();
    if (del) delete graph;
  }
  delete s->session;
  delete s;
}

void TF_SessionRun(TF_Session* session, const TF_Buffer* run_options,

 *  Deserialization, on the other hand, is performed by a {@link org.gradle.internal.serialize.graph.DecodingProvider}.
 *  </p>
 *  <p>Both protocols are highly specialized and, as such, they are specified as Single Abstract Method interfaces.</p>
 *
 *  <h3>Codecs</h3>

#include "tensorflow/compiler/mlir/tensorflow/translate/mlir_roundtrip_flags.h"
#include "tensorflow/core/framework/function.h"
#include "tensorflow/core/framework/graph.pb.h"
#include "tensorflow/core/framework/node_def.pb.h"
#include "tensorflow/core/graph/graph.h"

namespace tensorflow {

ABSL_DEPRECATED("Use tensorflow::tf2xla::api::ConvertMlirToGraphdef instead.")
// Given an MLIR module, returns a GraphDef.

  TF_Status* s = TF_NewStatus();
  TF_Graph* graph = TF_NewGraph();

  // Create a simple graph.
  Placeholder(graph, s);
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
  ASSERT_TRUE(TF_GraphOperationByName(graph, "feed") != nullptr);
  TF_Operation* oper = ScalarConst(3, graph, s);
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
  ASSERT_TRUE(TF_GraphOperationByName(graph, "scalar") != nullptr);
  Neg(oper, graph, s);

              'Cannot find the output tensor with name %s in the graph.'
              % tensor_info.name
          ) from exc

  return signature_def_map


def _find_op(
    graph: ops.Graph, op_name: Optional[str]
) -> Optional[ops.Operation]:
  """Finds the operation with `op_name`.

  Args:
    graph: The graph to find from.
    op_name: Name of the node.

  Returns:

import org.gradle.internal.serialize.graph.BeanStateReaderLookup
import org.gradle.internal.serialize.graph.BeanStateWriterLookup
import org.gradle.internal.serialize.graph.ClassDecoder
import org.gradle.internal.serialize.graph.ClassEncoder
import org.gradle.internal.serialize.graph.DefaultReadContext
import org.gradle.internal.serialize.graph.DefaultWriteContext
import org.gradle.internal.serialize.graph.IsolateOwner

Scope Scope::NewRootScope() {
  Graph* graph = new Graph(OpRegistry::Global());
  ShapeRefiner* refiner =
      new ShapeRefiner(graph->versions(), graph->op_registry());
  return Scope(new Impl(graph, new Status, new Impl::NameMap, refiner,
                        /* disable_shape_inference */ false));
}

Scope Scope::DisabledShapeInferenceScope() {
  Graph* graph = new Graph(OpRegistry::Global());
  ShapeRefiner* refiner =

import org.gradle.internal.serialize.graph.Codec
import org.gradle.internal.serialize.graph.ReadContext
import org.gradle.internal.serialize.graph.WriteContext
import org.gradle.internal.serialize.graph.decodePreservingSharedIdentity
import org.gradle.internal.serialize.graph.encodePreservingSharedIdentityOf
import org.gradle.internal.serialize.graph.readList
import org.gradle.internal.serialize.graph.readNonNull