absl::Span<AbstractTensorHandle* const> inputs,
                             std::vector<AbstractTensorHandle*>* params);

// A callable that takes tensor inputs and returns zero or more tensor outputs.
using Model = std::function<Status(AbstractContext*,
                                   absl::Span<AbstractTensorHandle* const>,
                                   absl::Span<AbstractTensorHandle*>)>;

			}
			return
		}

		// Log only long List requests (ignore Watch).
		defer span.End(500 * time.Millisecond)
		span.AddEvent("About to List from storage")
		result, err := r.List(ctx, &opts)
		if err != nil {
			scope.err(err, w, req)
			return
		}
		span.AddEvent("Listing from storage done")
		defer span.AddEvent("Writing http response done", attribute.Int("count", meta.LenList(result)))

++++
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++++

== For Software Developers

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++++

Build engineers that are ready to configure custom build logic and write their own plugins should start here.

const (
	_ traceEv = 127 + iota

	// Experimental events for ExperimentAllocFree.

	// Experimental heap span events. IDs map reversibly to base addresses.
	traceEvSpan      // heap span exists [timestamp, id, npages, type/class]
	traceEvSpanAlloc // heap span alloc [timestamp, id, npages, type/class]
	traceEvSpanFree  // heap span free [timestamp, id]

	// Experimental heap object events. IDs map reversibly to addresses.

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    TF_DeleteTensor(numerical_tensor);
    delete[] danalytical;
    delete[] dnumerical;
    outputs[i]->Unref();
  }
}

void CheckTensorValue(AbstractTensorHandle* t, absl::Span<const float> manuals,
                      absl::Span<const int64_t> dims, double abs_error) {
  TF_Tensor* analytical_tensor;
  auto s = GetValue(t, &analytical_tensor);
  ASSERT_EQ(errors::OK, s.code()) << s.message();

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

Status SparseSoftmaxCrossEntropyWithLogitsModel(
    AbstractContext* ctx, absl::Span<AbstractTensorHandle* const> inputs,
    absl::Span<AbstractTensorHandle*> outputs) {
  AbstractTensorHandle* loss;

		return fmt.Errorf("PrepareObjectForStorage failed: %v", err)
	}
	span.AddEvent("About to Encode")
	data, err := runtime.Encode(s.codec, obj)
	if err != nil {
		span.AddEvent("Encode failed", attribute.Int("len", len(data)), attribute.String("err", err.Error()))
		return err
	}
	span.AddEvent("Encode succeeded", attribute.Int("len", len(data)))

	opts, err := s.ttlOpts(ctx, int64(ttl))

					Addresses: []string{"1.2.3.4"},
					Hosts:     []string{"a.example.com", "b.example.com"},
					Ports: []*networking.ServicePort{{
						Number: 80,
						Name:   "http",
					}},
					SubjectAltNames: []string{"san1"},
					Resolution:      networking.ServiceEntry_DNS,
				},
			},
			result: []*workloadapi.Service{
				{
					Name:      "name",
					Namespace: "ns",
					Hostname:  "a.example.com",