Klaus Post <******@****.***> 1756435188 +0200

ILIYA <******@****.***> 1748359143 +0900

Klaus Post <******@****.***> 1756435188 +0200

     */
    runConcurrentlyMutatedTest(elements(), ops(add(1), add(2)), wrap);

    runConcurrentlyMutatedTest(elements(), ops(add(1), nop()), wrap);

    runConcurrentlyMutatedTest(elements(), ops(add(1), remove()), wrap);

    runConcurrentlyMutatedTest(elements(), ops(nop(), add(1)), wrap);

    runConcurrentlyMutatedTest(elements(1), ops(remove(), nop()), wrap);

					n += got
				}
				atomic.AddInt64(&ops, int64(n))
				atomic.AddInt64(&lat, latency)
			})
			spent := time.Since(t)
			if spent > 0 && n > 0 {
				// Since we are benchmarking n parallel servers we need to multiply by n.
				// This will give an estimate of the total ops/s.
				latency := float64(atomic.LoadInt64(&lat)) / float64(time.Millisecond)
				b.ReportMetric(float64(n)*float64(ops)/spent.Seconds(), "vops/s")

  return ops::MatMul(ctx, inputs[0], inputs[1], &outputs[0],
                     /*transpose_a=*/false,
                     /*transpose_b=*/false, "MatMul");
}

absl::Status MulModel(AbstractContext* ctx,
                      absl::Span<AbstractTensorHandle* const> inputs,
                      absl::Span<AbstractTensorHandle*> outputs) {
  return ops::Mul(ctx, inputs[0], inputs[1], &outputs[0], "Mul");
}

    TF_RETURN_IF_ERROR(
        ops::Sub(ctx, fPlus.get(), fMinus.get(), f_outputs, "sub_top"));
    AbstractTensorHandlePtr fDiff(f_outputs[0]);

    // Calculate using the difference quotient definition:
    // (f(theta + eps) - f(theta - eps)) / (2 * eps).
    TF_RETURN_IF_ERROR(
        ops::Div(ctx, fDiff.get(), two_eps.get(), f_outputs, "diff_quotient"));

#include "tensorflow/c/experimental/gradients/not_differentiable.h"
#include "tensorflow/c/experimental/gradients/tape/tape_context.h"
#include "tensorflow/c/experimental/ops/array_ops.h"
#include "tensorflow/c/experimental/ops/math_ops.h"
#include "tensorflow/c/tf_status_helper.h"
#include "tensorflow/c/tf_tensor.h"
#include "tensorflow/core/lib/llvm_rtti/llvm_rtti.h"
#include "tensorflow/core/platform/errors.h"

# A shared object which includes registration mechanisms for ops and
# kernels. Does not include the implementations of any ops or kernels. Instead,
# the library which loads libtensorflow_framework.so
# (e.g. _pywrap_tensorflow_internal.so for Python, libtensorflow.so for the C
# API) is responsible for registering ops with libtensorflow_framework.so. In
# addition to this core set of ops, user libraries which are loaded (via

    TFE_Executor*, TF_Status* status);

// When an error happens, any pending operations are discarded, and newly issued
// ops return an error. This call clears the error state and re-enables
// execution of newly issued ops.
//
// Note that outputs of discarded ops remain in a corrupt state and should not
// be used for future calls.
// TODO(agarwal): mark the affected handles and raise errors if they are used.