/*expected_max_outputs=*/1, status.get());
  ASSERT_FALSE(TF_GetCode(status.get()) == TF_OK);
  TF_SetStatus(status.get(), TF_OK, "");

  // Check that ops still run successfully on the device.
  parallel_device.Execute(context.get(), std::vector<ParallelTensor*>(),
                          "VarHandleOp", TFE_OpGetAttrs(handle_op.get()),

#include "tensorflow/c/experimental/filesystem/modular_filesystem.h"
#include "tensorflow/cc/framework/gradients.h"
#include "tensorflow/cc/framework/ops.h"
#include "tensorflow/cc/framework/scope_internal.h"
#include "tensorflow/cc/ops/while_loop.h"
#include "tensorflow/cc/saved_model/loader.h"
#include "tensorflow/core/distributed_runtime/server_lib.h"
#include "tensorflow/core/framework/logging.h"

     * started nor stopped.
     */
    FAILED,
  }

  /**
   * A listener for the various state changes that a {@link Service} goes through in its lifecycle.
   *
   * <p>All methods are no-ops by default, implementors should override the ones they care about.
   *
   * @author Luke Sandberg
   * @since 15.0 (present as an interface in 13.0)
   */
  abstract class Listener {
    /**

models or graphs is equivalent to running untrusted code.

If you need to run untrusted models, execute them inside a
[**sandbox**](https://developers.google.com/code-sandboxing). Memory corruptions
in TensorFlow ops can be recognized as security issues only if they are
reachable and exploitable through production-grade, benign models.

### Compilation

Compiling models via the recommended entry points described in

	defer func(old int) { buildcfg.GOPPC64 = old }(buildcfg.GOPPC64)
	for _, goppc64 := range []int{8, 9, 10} {
		t.Logf("GOPPC64=power%d", goppc64)
		buildcfg.GOPPC64 = goppc64
		// Some pseudo-ops may assemble differently depending on GOPPC64
		testEndToEnd(t, "ppc64", "ppc64")
		testEndToEnd(t, "ppc64", "ppc64_p10")
	}
}

func TestRISCVEndToEnd(t *testing.T) {
	testEndToEnd(t, "riscv64", "riscv64")
}

      ASSERT_EQ(expected[i], data[i]) << "Different data at index " << i;
    }
  }

  std::vector<TF_Output> ToOutput(const std::vector<TF_Operation*> ops) {
    std::vector<TF_Output> out;
    for (auto op : ops) {
      out.push_back({op, 0});
    }
    return out;
  }

  void Define(int num_opers, const std::vector<TF_Operation*>& opers,

                "value": null
              },
              {
                "color": "red",
                "value": 80
              }
            ]
          },
          "unit": "ops"
        },
        "overrides": []
      },
      "gridPos": {
        "h": 4,
        "w": 8,
        "x": 0,
        "y": 4
      },
      "id": 12,
      "links": [],

         "type": "row"
      }
   ],
   "schemaVersion": 39,
   "templating": {
      "list": [
         {
            "name": "datasource",
            "query": "prometheus",
            "regex": "(ops|dev)-cortex",
            "type": "datasource"
         },
         {
            "datasource": {
               "type": "prometheus",
               "uid": "${datasource}"
            },

                  "gradientMode": "none",
                  "showPoints": "never",
                  "stacking": {
                     "mode": "normal"
                  }
               },
               "unit": "ops"
            },
            "overrides": [
               {
                  "matcher": {
                     "id": "byName",
                     "options": "cds"
                  },

// --------------------------------------------------------------------------
// Load plugins containing custom ops and kernels

// TF_Library holds information about dynamically loaded TensorFlow plugins.
typedef struct TF_Library TF_Library;

// Load the library specified by library_filename and register the ops and
// kernels present in that library.
//