*       APIs are offered that don't).
 *   <li>Can't be passed directly to methods that expect {@code double[]} (though the most common
 *       utilities do have replacements here).
 *   <li>Dependency on {@code com.google.common} / Guava.
 * </ul>
 *
 * <p>Advantages compared to {@link com.google.common.collect.ImmutableList ImmutableList}{@code
 * <Double>}:
 *
 * <ul>

 *       APIs are offered that don't).
 *   <li>Can't be passed directly to methods that expect {@code int[]} (though the most common
 *       utilities do have replacements here).
 *   <li>Dependency on {@code com.google.common} / Guava.
 * </ul>
 *
 * <p>Advantages compared to {@link com.google.common.collect.ImmutableList ImmutableList}{@code
 * <Integer>}:
 *
 * <ul>

"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"preSetPlacement",enabled:!0,phase:"beforeMain",fn:t=>{this._getTipElement().setAttribute("data-popper-placement",t.state.placement)}}]};return{...e,...v(this._config.popperConfig,[e])}}_setListeners(){const t=this._config.trigger.split(" ");for(const e of t)if("click"===e)F.on(this._element,this.constructor.eventName("click"),this._config.selector,(t=>{this._initi...

	diskFillFraction = 0.99

	// diskReserveFraction is the fraction of a disk where we will fill other server pools first.
	// If all pools reach this, we will use all pools with regular placement.
	diskReserveFraction = 0.15

	// diskAssumeUnknownSize is the size to assume when an unknown size upload is requested.
	diskAssumeUnknownSize = 1 << 30

  TFE_Execute(matmul.get(), &retval, &num_retvals, status.get());
  EXPECT_TRUE(executed);
  ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());
  TFE_DeleteTensorHandle(retval);

  // Explicit placement still forces the op onto the requested device
  matmul.reset(MatMulOp(context.get(), hcustom0.get(), hcpu.get()));
  TFE_OpSetDevice(matmul.get(), "/job:localhost/replica:0/task:0/device:CPU:0",
                  status.get());

append(newDecls, decl) } f.Decls = newDecls // Replace identifiers. ast.Inspect(f, func(n ast.Node) bool { switch x := n.(type) { case *ast.Ident: if replacement, ok := replacements[x.Name]; ok { x.Name = replacement } } return true }) // Replace identifiers in comments. for _, c := range f.Comments { for _, l := range c.List { for k, v := range replacements { if k == "k" { continue } l.Text = strings.ReplaceAll(l.Text, k, v) } } } out, err := os.Create(*outputFile) if err != nil { log.Fatal(err) } defer...

    enabled: true,\n          phase: 'beforeMain',\n          fn: data => {\n            // Pre-set Popper's placement attribute in order to read the arrow sizes properly.\n            // Otherwise, Popper mixes up the width and height dimensions since the initial arrow style is for top placement\n            this._getTipElement().setAttribute('data-popper-placement', data.state.placement)\n          }\n        }\n      ]\n    }\n\n    return {\n      ...defaultBsPopperConfig,\n      ...execute(...

     *
     * <ul>
     *   <li>If {@link #CLASSES} path type is also set, then the dependency should be placed on the
     *       module path, but is also compatible with placement on the class path. Compatibility can
     *       be achieved, for example, by repeating in the {@code META-INF/services/} directory the services

        self.reset()
        self.text_parts = []
        self.feed(html)
        return "".join(self.text_parts).strip()


def slugify(text: str) -> str:
    return py_slugify(
        text,
        replacements=[
            ("`", ""),  # `dict`s -> dicts
            ("'s", "s"),  # it's -> its
            ("'t", "t"),  # don't -> dont
            ("**", ""),  # **FastAPI**s -> FastAPIs
        ],
    )

  // Register graph pass that will raise error if called more than once.
  tensorflow::optimization_registration::OptimizationPassRegistration
      register_test_pass(tensorflow::OptimizationPassRegistry::PRE_PLACEMENT, 0,
                         std::make_unique<GraphErrorInjectionPass>(),
                         "error_injector");
  GraphErrorInjectionPass::enabled_ = true;

  tensorflow::ServerDef server_def = GetServerDef(3);