// First create a simple identity function.
  TF_Graph* function_graph = TF_NewGraph();
  TF_OperationDescription* arg_descr =
      TF_NewOperation(function_graph, "Placeholder", "arg");
  TF_SetAttrType(arg_descr, "dtype", TF_INT32);
  TF_Status* status = TF_NewStatus();
  TF_Operation* arg = TF_FinishOperation(arg_descr, status);
  ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status);
  TF_OperationDescription* id_descr =

  // will be blocked till the copy completes. This is the default placement
  // policy.
  TFE_DEVICE_PLACEMENT_SILENT = 2,
  // Placement policy which silently copies int32 tensors but not other dtypes.
  TFE_DEVICE_PLACEMENT_SILENT_FOR_INT32 = 3,
} TFE_ContextDevicePlacementPolicy;
// LINT.ThenChange(//tensorflow/c/eager/immediate_execution_context.h)

// Sets the default execution mode (sync/async). Note that this can be

# ADR-0003 - Avoid introducing Groovy types to public API

## Date

2024-01-12

## Context

Gradle's public API requires equal access from all JVM-based languages.
Kotlin, Groovy, Java, and other JVM-based languages should be able to use the Gradle API without relying on another language's standard library.

Historically, Gradle has shipped with some Groovy types in very prominent APIs.

  TF_Status* status = TF_NewStatus();
  TFE_Op* op = TFE_NewOp(ctx, "ReadVariableOp", status);
  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TFE_OpSetAttrType(op, "dtype", TF_FLOAT);
  TFE_OpAddInput(op, var, status);
  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  int num_retvals = 1;
  TFE_Execute(op, out_value, &num_retvals, status);

    forward_op_->outputs.push_back(retvals[i]);
  }
  // TODO(b/166669239): This is needed to support AttrBuilder::Get for string
  // attributes. Number type attrs and DataType attrs work fine without this.
  // Consider getting rid of this and making the behavior between number types
  // and string consistent.
  forward_op_->attrs.BuildNodeDef();
  std::unique_ptr<GradientFunction> gradient_fn;

				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{

return values are mapped to functions returning a struct.

Not all Go types can be mapped to C types in a useful way.
Go struct types are not supported; use a C struct type.
Go array types are not supported; use a C pointer.

Go functions that take arguments of type string may be called with the
C type _GoString_, described above. The _GoString_ type will be
automatically defined in the preamble. Note that there is no way for C

---

## **Typer**, o FastAPI das interfaces de linhas de comando

<a href="https://typer.tiangolo.com" target="_blank"><img src="https://typer.tiangolo.com/img/logo-margin/logo-margin-vector.svg" style="width: 20%;"></a>

                                                            TF_DeleteGraph);
  TF_OperationDescription* placeholder_desc =
      TF_NewOperation(body.get(), "Placeholder", "Placeholder");
  TF_SetAttrType(placeholder_desc, "dtype", TF_FLOAT);
  TF_Operation* placeholder_op = TF_FinishOperation(placeholder_desc, status);
  if (TF_GetCode(status) != TF_OK) return;
  TF_Output x{placeholder_op, 0};

  TF_OperationDescription* reduce_desc =

        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