Graph graph(flib_def);
  graph.SetConstructionContext(ConstructionContext::kDirectSession);

  Scope root = Scope::NewRootScope().ExitOnError();

  Output x = ops::Placeholder(root.WithOpName("x"), DT_FLOAT);
  NameAttrList f_name_attr;
  f_name_attr.set_name("XTimesTwoFloat");
  ops::TPUPartitionedCall f(root.WithOpName("f"), {x}, /*device_ordinal=*/0,

	return volumetypes.NewOperationContext(fmt.Errorf("placeholder1"), fmt.Errorf("placeholder2"), false)
}

func generateWaitWithErrorFunc(done <-chan interface{}) func() volumetypes.OperationContext {
	return func() volumetypes.OperationContext {
		<-done
		return volumetypes.NewOperationContext(fmt.Errorf("placeholder1"), fmt.Errorf("placeholder2"), false)
	}
}

func noopFunc() volumetypes.OperationContext {

	queued *sparseSet   // has been put in q
	hasPhi *sparseSet   // has a phi
	hasDef *sparseSet   // has a write of the variable we're processing

	// miscellaneous
	placeholder *ssa.Value // value to use as a "not set yet" placeholder.
}

func (s *phiState) insertPhis() {
	if debugPhi {
		fmt.Println(s.f.String())
	}

	// Find all the variables for which we need to match up reads & writes.

  NodeDef call_node_def;
  call_node_def.set_op("0");
  TF_CHECK_OK(
      rewrite_fn(arg_source_tensors, &g, nullptr, nullptr, &call_node_def));
  node_name_image = g->BuildNodeNameIndex();

  // Verify step 1: add key placeholder node.
  Node *key_placeholder = node_name_image["cluster_key_placeholder"];
  EXPECT_NE(key_placeholder, nullptr);
  // Verify step 2: replace _Arg nodes with XlaRecvAtHost.
  for (Node *n : g->nodes()) {

Use the link https://www.slf4j.org/manual.html#typical_usage[typical SLF4J pattern] to replace a placeholder with an actual value in the log message.

====
include::sample[dir="snippets/tutorial/logging/kotlin",files="build.gradle.kts[tags=use-logger-placeholder]"]
include::sample[dir="snippets/tutorial/logging/groovy",files="build.gradle[tags=use-logger-placeholder]"]
====

    DCHECK(inputs_.empty());
    DCHECK_GT(ninputs, 0);

    for (int i = 0; i < ninputs; ++i) {
      TF_Operation* placeholder = Placeholder(
          graph_, s_, ::tensorflow::strings::StrCat("p", i).c_str());
      DCHECK_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
      inputs_.push_back({placeholder, 0});
    }

    original_graph_description_ = GraphDebugString();

    params_ = std::make_unique<TF_WhileParams>(

```

```
mc admin config set myminio logger_webhook:name1 auth_token="" endpoint="http://endpoint:port/path"
mc admin service restart myminio
```

NOTE: `http://endpoint:port/path` is a placeholder value to indicate the URL format, please change this accordingly as per your configuration.

  TF_OperationDescription* desc = TF_NewOperation(graph, "Placeholder", name);
  TF_SetAttrType(desc, "dtype", dtype);
  if (!dims.empty()) {
    TF_SetAttrShape(desc, "shape", dims.data(), dims.size());
  }
  *op = TF_FinishOperation(desc, s);
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
  ASSERT_NE(*op, nullptr);
}

TF_Operation* Placeholder(TF_Graph* graph, TF_Status* s, const char* name,

        metadata.selectLegacyConfigurations(fromConfig3, toComponent, resolutionFailureHandler).variants == [toConfig2]
    }

    def "configuration mapping can include self placeholder on RHS"() {
        def resolutionFailureHandler = Stub(ResolutionFailureHandler)
        def toComponent = Stub(ComponentGraphResolveState)
        def fromConfig = Stub(ModuleConfigurationMetadata)

#include "tensorflow/core/util/equal_graph_def.h"

namespace tensorflow {
namespace {

using ops::Assign;
using ops::Const;
using ops::Identity;
using ops::MatMul;
using ops::OnesLike;
using ops::Placeholder;
using ops::Square;
using ops::Stack;
using ops::StopGradient;
using ops::Unstack;
using ops::Variable;

// TODO(andydavis) Add more unit tests once more gradient functions are ported.