TF_AbstractTensor* TF_AddFunctionParameter(TF_ExecutionContext* func,
                                           TF_DataType dtype, TF_Shape shape,
                                           TF_Status* s);

// Create an operation suitable to use with the provided context. The operation
// requires its type (e.g. "AddV2") to be set independently.
TF_AbstractOp* TF_NewAbstractOp(TF_ExecutionContext* ctx);
void TF_DeleteAbstractOp(TF_AbstractOp*);

                    <mxGeometry x="710" y="-50" width="300" height="80" as="geometry"/>
                </mxCell>

    }

    public static String createBulkLine(final String index, final String type, final SuggestItem item) {
        final Map<String, Object> firstLineMap = new HashMap<>();
        final Map<String, Object> firstLineInnerMap = new HashMap<>();
        firstLineInnerMap.put("_index", index);
        firstLineInnerMap.put("_type", type);
        firstLineInnerMap.put("_id", item.getId());

        the layer's dtype. If you had a model that used `float64`, it will
        probably silently use `float32` in TensorFlow2, and a warning will be
        issued that starts with Layer "layer-name" is casting an input tensor
        from dtype float64 to the layer's dtype of float32. To fix, either set
        the default dtype to float64 with

### Testado

* 100% <abbr title="A quantidade de código que é testada automaticamente">de cobertura de testes</abbr>.
* 100% do código utiliza <abbr title="Type annotations do Python, com isso seu editor e ferramentas externas podem te dar um suporte melhor">type annotations</abbr>.
* Usado para aplicações em produção.

## Recursos do Starlette

        MsrpcShareInfo1 ( srvsvc.ShareInfo1 info1 ) {
            this.netName = info1.netname;
            this.type = info1.type;
            this.remark = info1.remark;
        }
    }


    public MsrpcShareEnum ( String server ) {
        super("\\\\" + server, 1, new srvsvc.ShareInfoCtr1(), -1, 0, 0);
        this.ptype = 0;
        this.flags = DCERPC_FIRST_FRAG | DCERPC_LAST_FRAG;
    }

        MsrpcShareInfo1(srvsvc.ShareInfo1 info1) {
            this.netName = info1.netname;
            this.type = info1.type;
            this.remark = info1.remark;
        }
    }

    public MsrpcShareEnum(String server) {
        super("\\\\" + server, 1, new srvsvc.ShareInfoCtr1(), -1, 0, 0);
        ptype = 0;
        flags = DCERPC_FIRST_FRAG | DCERPC_LAST_FRAG;
    }

    public FileEntry[] getEntries() {

                    enc-part
                        etype: eTYPE-ARCFOUR-HMAC-MD5 (23)
                        kvno: 5
                        cipher: 508c1fb7944c336ead0edf4536ba1ecf7102923d9dc9e0ab1d5c73d0d3bb4ca6a1120cdd…
                authenticator
                    etype: eTYPE-ARCFOUR-HMAC-MD5 (23)

      gtl::ArraySlice<AbstractTensorHandle*> output_gradients,
      absl::Span<AbstractTensorHandle*> result) const override;

  // Builds a tensor filled with ones with the same shape and dtype as `t`.
  absl::Status BuildOnesLike(const TapeTensor& t,
                             AbstractTensorHandle** result) const override;

  // Looks up the ID of a Gradient.

				MetricV2{
					Description: MetricDescription{
						Namespace: bucketMetricNamespace,
						Subsystem: "batch",
						Name:      MetricName(jtype + "_objects"),
						Help:      "Get successfully completed batch job " + jtype + "objects",
						Type:      counterMetric,
					},
					Value:          objects,
					VariableLabels: map[string]string{"bucket": bucket, "jobId": mj.JobID},
				},
				MetricV2{