} else if (auto island =
                   llvm::dyn_cast<mlir::tf_executor::IslandOp>(inst)) {
      Operation& inner_op = island.GetBody().front();
      auto op_name = GetTensorFlowOpName(inner_op.getName().getStringRef());
      if (llvm::isa<FuncOp>(inner_op) && op_name.ok()) {
        // If it is TF Control dialect specific op, look up custom operation

  } else if (auto sink = llvm::dyn_cast<tf_executor::NextIterationSinkOp>(op)) {
    return ops_have_same_device(sink.GetSource(), sink);
  }

  return llvm::isa<tf_executor::EnterOp, tf_executor::ExitOp,
                   tf_executor::IslandOp, tf_executor::MergeOp,
                   tf_executor::SwitchOp>(op);
}

// Assigns all data results to a specified device.
void PopulateDeviceForOpResults(

    /**
     * Indicates whether the model building should happen in two phases. If enabled, the initial invocation of the model
     * builder will only produce an interim result which may be used to analyze inter-model dependencies before the
     * final invocation of the model builder is performed.
     *

  // the merged pad will not change anything, while the un-merged will remove a
  // value, then insert a 0 at its place. This only holds for low and high pads,
  // the spec does not allow negative interior pads, so we don't check there.
  auto low_pads = pad_op.getEdgePaddingLow().getValues<IntegerAttr>();
  auto parent_low_pads =
      parent_pad.getEdgePaddingLow().getValues<IntegerAttr>();

Porém, quando você registrar um manipulador de exceção, você deve registrá-lo através do `HTTPException` do Starlette.

Dessa forma, se qualquer parte do código interno, extensão ou plug-in do Starlette lançar o `HTTPException`, o seu manipulador de exceção poderá capturar esse lançamento e tratá-lo.

```Python
from starlette.exceptions import HTTPException as StarletteHTTPException
```

using ::tensorflow::quantization::PyFunctionLibrary;

bool IsControlFlowV1Op(Operation* op) {
  return mlir::isa<mlir::tf_executor::SwitchOp, mlir::tf_executor::MergeOp,
                   mlir::tf_executor::EnterOp, mlir::tf_executor::ExitOp,
                   mlir::tf_executor::NextIterationSinkOp,
                   mlir::tf_executor::NextIterationSourceOp>(op);
}

mlir::LogicalResult IsValidGraph(mlir::ModuleOp module) {

    // as well. For example there is Enter and RefEnter op. RefEnter forwards
    // the input ref buffer to output. However both Enter and RefEnter are
    // mapped to tf_executor::EnterOp during import. Check if it is a Ref op to
    // correctly map to the TensorFlow Graph op.
    if (IsRefTypeControlOp(inst)) op_name = "Ref";
    TF_ASSIGN_OR_RETURN(auto tf_name,

	github.com/jcmturner/gokrb5/v8 v8.4.4 // indirect
	github.com/jcmturner/rpc/v2 v2.0.3 // indirect
	github.com/jedib0t/go-pretty/v6 v6.5.9 // indirect
	github.com/jessevdk/go-flags v1.5.0 // indirect
	github.com/josharian/intern v1.0.0 // indirect
	github.com/juju/ratelimit v1.0.2 // indirect
	github.com/kr/fs v0.1.0 // indirect
	github.com/lestrrat-go/backoff/v2 v2.0.8 // indirect
	github.com/lestrrat-go/blackmagic v1.0.2 // indirect

Então, você rodaria **vários contêineres** com coisas diferentes, como um banco de dados, uma aplicação Python, um servidor web com uma aplicação frontend React, e conectá-los juntos via sua rede interna.

Todos os sistemas de gerenciamento de contêineres (como Docker ou Kubernetes) possuem essas funcionalidades de rede integradas a eles.

## Contêineres e Processos

class SliceDenseIntElementsAttr<string index, string axis> : NativeCodeCall<
  "SliceDenseIntElementsAttr($0.cast<ElementsAttr>(), " # index # ", " # axis # ")">;

// Interior padding attribute based on the TF padding.
def GetInteriorPadding : NativeCodeCall <
  "GetInteriorPadding($0.cast<ElementsAttr>())">;

def : Pat<(TF_PadV2Op $input, (ConstantLikeMatcher ElementsAttr:$padding), $c),