func TestAsin(t *testing.T) {
	for i := 0; i < len(vf); i++ {
		a := vf[i] / 10
		if f := Asin(a); !veryclose(asin[i], f) {
			t.Errorf("Asin(%g) = %g, want %g", a, f, asin[i])
		}
	}
	for i := 0; i < len(vfasinSC); i++ {
		if f := Asin(vfasinSC[i]); !alike(asinSC[i], f) {
			t.Errorf("Asin(%g) = %g, want %g", vfasinSC[i], f, asinSC[i])
		}
	}
}

func TestAsinh(t *testing.T) {

                 std::vector<Output>* grad_outputs) {
  // y = asinh(x)
  // dy/dx = 1 / cosh(y)
  auto dydx = Reciprocal(scope, Cosh(scope, op.output(0)));
  // grad(x) = grad(y) * conj(dy/dx)
  grad_outputs->push_back(
      Mul(scope, grad_inputs[0], ConjugateHelper(scope, dydx)));
  return scope.status();
}
REGISTER_GRADIENT_OP("Asinh", AsinhGrad);

Status AcoshGrad(const Scope& scope, const Operation& op,

}

absl::flat_hash_set<string> GetKnownXLAAllowlistOp() {
  absl::flat_hash_set<string> result{
      "AdjustContrastv2",
      "AdjustHue",
      "AdjustSaturation",
      "Asinh",
      "Assert",
      "AssignAddVariableOp",
      "AssignSubVariableOp",
      "AssignVariableOp",
      "AssignVariableXlaConcatND",
      "AvgPool",
      "AvgPool3D",
      "AvgPool3DGrad",

func.func @_UnaryOpsComposition(%arg0: tensor<4xf32>) -> tensor<4xf32> {

  // CHECK: %[[RESULT0:.*]] = "tf.Asin"(%[[ARG0]])
  // CHECK: %[[RESULT1:.*]] = "tf.Abs"(%[[RESULT0]])
  // CHECK: %[[RESULT2:.*]] = "tf.Log"(%[[RESULT1]])
  // CHECK: return %[[RESULT2]]

  %0 = "tf._UnaryOpsComposition"(%arg0) {op_names = ["Asin", "Abs", "Log"]} : (tensor<4xf32>) -> tensor<4xf32>
  func.return %0 : tensor<4xf32>
}

                t1.propertyAssignment(null, "languageVersion",
                    new MethodInvocationExpression(null, "JavaLanguageVersion.of", singletonList(new LiteralValue(languageVersion.asInt()))),
                    true);
            });
        });
    }

    /**
     * Adds a method invocation statement to the configuration of a particular task.
     */

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