%4 = "tf.LayerNorm"(%a1, %a2, %a3, %a4) {_tfl_quant_trait = "fully_quantizable", device = ""} : (tensor<128x128xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xi32>) -> tensor<128x128xf32>
   "tfl.yield"(%4) : (tensor<128x128xf32>) -> ()
  }) {_tfl_quant_trait = "fully_quantizable", device = ""} : (tensor<128x128xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xi32>) -> tensor<128x128xf32>

		switch {
		case cb.Params().Len() > 2:
			return bad("func must be func(yield func(...) bool): yield func has too many parameters")
		case cb.Results().Len() != 1 || !isBoolean(cb.Results().At(0).Type()):
			return bad("func must be func(yield func(...) bool): yield func does not return bool")
		}
		if cb.Params().Len() >= 1 {
			key = cb.Params().At(0).Type()
		}
		if cb.Params().Len() >= 2 {

		switch {
		case cb.Params().Len() > 2:
			return bad("func must be func(yield func(...) bool): yield func has too many parameters")
		case cb.Results().Len() != 1 || !isBoolean(cb.Results().At(0).Type()):
			return bad("func must be func(yield func(...) bool): yield func does not return bool")
		}
		if cb.Params().Len() >= 1 {
			key = cb.Params().At(0).Type()
		}
		if cb.Params().Len() >= 2 {

1. This is the generator function. It's a "generator function" because it contains `yield` statements inside.
2. By using a `with` block, we make sure that the file-like object is closed after the generator function is done. So, after it finishes sending the response.
3. This `yield from` tells the function to iterate over that thing named `file_like`. And then, for each part iterated, yield that part as coming from this generator function.

     *
     * Unfortunately, [symbol] for the `action("")` call will be pointing to the `Function1<P1, R>.invoke(p1: P1): R`, because we
     * intentionally unwrap use-site substitution overrides. Because of this, `symbol.name` will yield `"p1"`, and not `"bar"`.
     *
     * To overcome this problem, [name] property is introduced: it allows to get the intended name of the parameter,
     * with respect to `@ParameterName` annotation.
     *

        val parts = fqName.pathSegments()
        if (parts.isEmpty()) {
            yield(FqNameInterpretation.create(packageParts = emptyList(), classParts = emptyList(), callable = null))
            return@sequence
        }
        for (lastPackagePartIndexExclusive in 0..parts.size) {
            yield(
                FqNameInterpretation.create(

   * one from the pool. We must take the pooled connection because by taking it from the pool, we've
   * fully acquired it.
   *
   * This test yields threads to force the decision of plan1 to be deliberate and not lucky. In
   * particular, we set up this sequence of events:
   *
   *  1. take plan 0
   *  3. plan 0 connects
   *  4. finish taking plan 1
   *

        "tf.saved_model.save(tf_module, saved_model_dir)\n",
        "\n",
        "def calibration_dataset():\n",
        "  rng = np.random.default_rng(seed=1235)\n",
        "  for _ in range(2):\n",
        "    yield {\n",
        "        'lhs_operand': rng.uniform(low=-1.0, high=1.0, size=input_shape).astype(\n",
        "            np.float32\n",
        "        )\n",
        "    }\n",

                for (child in kotlinFile.declarations) {
                    if (child is KtScript) {
                        yieldAll(child.declarations)
                    } else {
                        yield(child)
                    }
                }
            }
        }

    override fun getClassLikeDeclarationByClassId(classId: ClassId): KtClassLikeDeclaration? {

										"field3": {
											Type: "string",
										},
										"field4": {
											Type: "string",
										},
									},
								},
							},
							SelectableFields: []apiextensions.SelectableField{
								{
									JSONPath: ".field3",
								},
								{
									JSONPath: ".field4",
								},
							},
						},
					},
				},
			},