*                 v   v
   */
  // Define
  TF_Operation* feed1 = Placeholder(func_graph_, s_, "feed1");
  TF_Operation* feed2 = Placeholder(func_graph_, s_, "feed2");
  Define(-1, {}, {feed1, feed2}, {feed2, feed1}, {});

  // Use, run, and verify
  TF_Operation* two = ScalarConst(2, host_graph_, s_);
  TF_Operation* func_feed = Placeholder(host_graph_, s_);
  TF_Operation* func_op = Use({two, func_feed});

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

  // Make a placeholder operation.
  TF_Operation* feed = Placeholder(graph, s);
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

  // Test TF_Operation*() query functions.
  EXPECT_EQ(string("feed"), string(TF_OperationName(feed)));
  EXPECT_EQ(string("Placeholder"), string(TF_OperationOpType(feed)));
  EXPECT_EQ(string(""), string(TF_OperationDevice(feed)));

  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,

        assertEquals(expectedNightlyPromotionTriggerHour, vsb.determineNightlyPromotionTriggerHour())
    }

    @ParameterizedTest
    @ValueSource(
        strings = [
            "experimental",
            "placeholder-1",
            "whatever",
        ],
    )
    fun branchesWithVcsTriggerDisabled(branchName: String) {
        val vsb = VersionedSettingsBranch(branchName)

}

void TF_SetAttrPlaceholder(TF_OperationDescription* desc, const char* attr_name,
                           const char* placeholder) {
  tensorflow::AttrValue attr_value;
  attr_value.set_placeholder(placeholder);
  desc->node_builder.Attr(attr_name, attr_value);
}

void TF_SetAttrFuncName(TF_OperationDescription* desc, const char* attr_name,

              --input $(location :tensorflow_def_file) \\
              --output $@
          """,
        "//conditions:default": "touch $@",  # Just a placeholder for Unix platforms
    }),
    tools = ["@local_config_def_file_filter//:def_file_filter"],
    visibility = ["//visibility:public"],
)

      session->graph, absl::StrCat("arg_tensor_enqueue_", tensor_id).c_str());
  if (placeholder_op == nullptr) {
    status->status = tensorflow::errors::Internal(
        "Unable to find the placeholder node as input to enqueue in the TF "
        "graph.");
    return;
  }

  VLOG(1) << "Running the enqueue op";
  TF_Output input{placeholder_op, 0};
  TF_SessionRun(session, /*run_options*/ nullptr,

    *   `tf.types.experimental.TraceType` now requires an additional `placeholder_value` method to be defined.
    *   `tf.function` now traces with placeholder values generated by TraceType instead of the value itself.

*   Experimental APIs `tf.config.experimental.enable_mlir_graph_optimization` and `tf.config.experimental.disable_mlir_graph_optimization` were removed.

</p>


<h3 id="Blank_identifier">Blank identifier</h3>

<p>
The <i>blank identifier</i> is represented by the underscore character <code>_</code>.
It serves as an anonymous placeholder instead of a regular (non-blank)
identifier and has special meaning in <a href="#Declarations_and_scope">declarations</a>,
as an <a href="#Operands">operand</a>, and in <a href="#Assignment_statements">assignment statements</a>.