// Tests that a quantized `stablehlo.pad` without interior padding is
// converted to `tfl.padv2`.

func.func @pad_without_interior_padding(
    %arg0: tensor<2x3x!quant.uniform<i8:f32, 2.000000e+00:-1>>,
    %arg1: tensor<!quant.uniform<i8:f32, 2.000000e+00:-1>>
  ) -> tensor<4x5x!quant.uniform<i8:f32, 2.000000e+00:-1>> {
  %0 = stablehlo.pad %arg0, %arg1, low = [0, 1], high = [2, 1], interior = [0, 0] : (

	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 {

look for link or image:

	find topmost [ or ![
	if none, emit literal ]
	if its inactive, remove and emit literal ]
	parse ahead to look for rest of link; if none, remove and emit literal ]
	run process emphasis on the interior,
	remove opener
	if this was a link (not an image), set all [ before opener to inactive, to avoid links inside links

process emphasis

	walk forward in list to find a closer.

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.

    %8 = "quantfork.dcast"(%7) : (tensor<!quant.uniform<i8:f32, 0.13170163023705575:-1>>) -> tensor<f32>
    %9 = stablehlo.pad %6, %8, low = [0, 1], high = [2, 1], interior = [0, 2] : (tensor<1x3xf32>, tensor<f32>) -> tensor<3x9xf32>
    %10 = "quantfork.qcast"(%9) {volatile} : (tensor<3x9xf32>) -> tensor<3x9x!quant.uniform<i8:f32, 0.13170163023705575:-1>>

        /*fused_activation_function=*/rewriter.getStringAttr("NONE"));
  }
};

// Rewrites quantized stablehlo.pad to tfl.padv2.
// tfl.dilate is introduced in between when interior padding exists.
// TODO: b/322428814 - Add StableHLO quantizer integration tests for ODML.
class RewriteQuantizedPadOp : public OpRewritePattern<stablehlo::PadOp> {
 public: