return
}

var methodValueCallFrameObjs [1]stackObjectRecord // initialized in stackobjectinit

func stkobjinit() {
	var abiRegArgsEface any = abi.RegArgs{}
	abiRegArgsType := efaceOf(&abiRegArgsEface)._type
	if abiRegArgsType.Kind_&abi.KindGCProg != 0 {
		throw("abiRegArgsType needs GC Prog, update methodValueCallFrameObjs")
	}
	// Set methodValueCallFrameObjs[0].gcdataoff so that

                         kSavedModelVariablesFilename);
        ImmediateTensorHandlePtr restored_output;
        TF_RETURN_IF_ERROR(internal::SingleRestore(
            context, variables_path_prefix, checkpoint_key, variable->dtype(),
            &restored_output));

        // Assign the restored tensor's value to the variable
        return variable->Assign(restored_output.get());
      }));

  return Status();
}

    auto iter = placeholders.find(placeholder_index);
    Node* placeholder_node;
    if (iter == placeholders.end()) {
      NodeDefBuilder placeholder_builder(new_name, "Placeholder");
      placeholder_builder.Attr("dtype", src->output_type(src_output));
      string outside_compilation_attr;
      TF_RETURN_IF_ERROR(GetNodeAttr(dst->attrs(),
                                     outside_compilation_attr_name,

}

// -----

// Tests that the pass can transform execution with model parallelism and no
// replication.
//
// The following TPUCompileMetadataProto is used:
// args {
//   dtype: DT_FLOAT
//   shape {
//     dim {
//       size: 128
//     }
//   }
// }
// num_replicas: 1
// num_cores_per_replica: 2

// CHECK-LABEL: func @parallel_execute

    TF_Status* status);

// Allocates a temporary Tensor of the specified type and shape. The
// Tensor must not be used after kernel construction is
// complete.
//
// num_dims must equal the size of array dims
TF_CAPI_EXPORT extern TF_Tensor* TF_AllocateTemp(
    TF_OpKernelContext* context, TF_DataType dtype, const int64_t* dims,
    int num_dims, TF_AllocatorAttributes* alloc_attrs, TF_Status* status);

// Specify this trait if the op requires all outputs to have the same type and
// the inputs either have the same type as result or a ref type corresponding to
// the result type.
def TF_OperandsSameAsResultsTypeOrRef : NativeOpTrait<
  "TF::OperandsSameAsResultsTypeOrRef">;

// Op has the same operand and result element types (or type itself, if scalar)
// after resolving reference types (i.e., after converting reference types to

  // 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 =

    ),
)

tf_cc_binary(
    name = "bef_executor",
    testonly = True,
    visibility = [":friends"],
    deps = [
        "@tf_runtime//:dtype",
        "@tf_runtime//:simple_tracing_sink",
        "@tf_runtime//tools:bef_executor_expensive_kernels",
        "@tf_runtime//tools:bef_executor_lib",
        "@tf_runtime//tools:bef_executor_lightweight_kernels",

### 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

	mask := v.mask
	if multipin {
		mask <<= 1
	}
	if val {
		atomic.Or8(v.bytep, mask)
	} else {
		atomic.And8(v.bytep, ^mask)
	}
}

// pinnerBits is the same type as gcBits but has different methods.
type pinnerBits gcBits

// ofObject returns the pinState of the n'th object.
// nosplit, because it's called by isPinned, which is nosplit
//
//go:nosplit