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),

// induceSubL_8_32 leaves behind only the leftmost L-type text
// index for each LMS-substring. That is, it removes the final S-type
// indexes that are present on entry, and it inserts but then removes
// the interior L-type indexes too.
// (Only the leftmost L-type index is needed by induceSubS_8_32.)
func induceSubL_8_32(text []byte, sa, freq, bucket []int32) {
	// Initialize positions for left side of character buckets.

		p = p[i:]
		if e.nbuf < 5 {
			return
		}
		e.enc.Encode(e.out[0:], e.buf[0:])
		if _, e.err = e.w.Write(e.out[0:8]); e.err != nil {
			return n, e.err
		}
		e.nbuf = 0
	}

	// Large interior chunks.
	for len(p) >= 5 {
		nn := len(e.out) / 8 * 5
		if nn > len(p) {
			nn = len(p)
			nn -= nn % 5
		}
		e.enc.Encode(e.out[0:], p[0:nn])
		if _, e.err = e.w.Write(e.out[0 : nn/5*8]); e.err != nil {

		// No change to our present, so no update of config file.
		return nil
	}

	// Flush our extended timeline back out to the configuration file.
	// If the configuration file has been updated in the interim,
	// we need to merge any updates made there as well.
	// Note that writeConfig is an atomic compare-and-swap.
	for {
		msg, err := c.ops.ReadConfig(c.name + "/latest")
		if err != nil {
			return err
		}

		p = p[i:]
		if e.nbuf < 3 {
			return
		}
		e.enc.Encode(e.out[:], e.buf[:])
		if _, e.err = e.w.Write(e.out[:4]); e.err != nil {
			return n, e.err
		}
		e.nbuf = 0
	}

	// Large interior chunks.
	for len(p) >= 3 {
		nn := len(e.out) / 4 * 3
		if nn > len(p) {
			nn = len(p)
			nn -= nn % 3
		}
		e.enc.Encode(e.out[:], p[:nn])
		if _, e.err = e.w.Write(e.out[0 : nn/3*4]); e.err != nil {

Ele também não controla modelos aninhados muito bem. Então, se o corpo JSON na requisição for um objeto JSON que contém campos internos que contém objetos JSON aninhados, ele não consegue ser validado e documentado apropriadamente.

!!! check "**FastAPI** inspirado para"
    Usar tipos Python para ter um ótimo suporte do editor.