* fit comfortably within a single ethernet packet (1500 bytes) even with framing overhead.
     *
     * For tests this must be big enough to realize real compression on test messages like
     * 'aaaaaaaaaa...'. Our tests check if compression was applied just by looking at the size if
     * the inbound buffer.
     */
    const val DEFAULT_MINIMUM_DEFLATE_SIZE = 1024L

// This transformation pass converts operations in TensorFlow dialect into
// operations that are legal in the TensorFlow Lite dialect.  Operations that
// can be legalized to TensorFlow Lite dialect with simple replacements are part
// of this pass and other operations that may create extra ops should be part of
// the PrepareTF pass which should be run before this pass.  That way any

   * granted only 100ms ago, then we wait for another 100ms. At this rate, serving 15 fresh permits
   * (i.e. for an acquire(15) request) naturally takes 3 seconds.
   *
   * It is important to realize that such a RateLimiter has a very superficial memory of the past:
   * it only remembers the last request. What if the RateLimiter was unused for a long period of

}

def TFR_TFRQuantRescaleOp : TFR_Op<"quant_rescale", [Pure]> {
  let description = [{
   The `quant_rescale` rescales the elements of the integer tensor by the
   floating-point rescale factor. This op needs to be legalized to the preferred
   operations of the backends.

    Example:

    ```mlir
    %3 = tfr.quant_rescale(%2, %1, %0) :
        (tfr.tensor, tfr.tensor, i64) -> (tfr.tensor)
    ```
  }];

                        lower_to_xla_hlo);

  if (lower_to_xla_hlo) {
    // This pass operates on MHLO control flow ops so it should be legalized
    // after the control flow ops are legalized.
    pm.addPass(mlir::mhlo::CreateLegalizeTFCommunicationPass());

    // Everything should be MHLO after this.
    if (!allow_partial_conversion) {
      pm.addNestedPass<mlir::func::FuncOp>(

# Implantação

Implantar uma aplicação **FastAPI** é relativamente fácil.

Existem vários modos de realizar o _deploy_ dependendo de seu caso de uso específico e as ferramentas que você utiliza.

Você verá mais sobre alguns modos de fazer o _deploy_ nas próximas seções.

## Versões do FastAPI

     * (Runtime checks could be particularly bad for users of LegacyConverter.)
     *
     * Luckily, our nullness checker is smart enough to realize that `convert` has @PolyNull-like
     * behavior, so it knows that `convert(a)` returns a non-nullable value, and we don't need to
     * perform even a cast, much less a runtime check.
     *

					assertInRange(t, "goal ratio", c[n-1].goalRatio(), 0.95, 1.05)
				}
			},
		},
		// TODO(mknyszek): Write a test that exercises the pacer's hard goal.
		// This is difficult in the idealized model this testing framework places
		// the pacer in, because the calculated overshoot is directly proportional
		// to the runway for the case of the expected work.

     * (Runtime checks could be particularly bad for users of LegacyConverter.)
     *
     * Luckily, our nullness checker is smart enough to realize that `convert` has @PolyNull-like
     * behavior, so it knows that `convert(a)` returns a non-nullable value, and we don't need to
     * perform even a cast, much less a runtime check.
     *

Doing respectively `launcher.get().executablePath`, `launcher.get().metadata.installationPath` or `compiler.get().executablePath` instead will give you the full path for the given toolchain but note that this may realize (and provision) a toolchain eagerly.

[[sec:auto_detection]]
== Auto-detection of installed toolchains