%cst = "tf.Const"() {value = dense<2.000000e+00> : tensor<f32>} : () -> tensor<f32>
    %0 = "tf.Mul"(%arg1, %arg0) : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
    %1 = "tf.Mul"(%0, %cst) : (tensor<1xf32>, tensor<f32>) -> tensor<1xf32>
    func.return %1 : tensor<1xf32>
  }

  // expected-remark@+1 {{failed to create tf2xla kernel: INVALID_ARGUMENT: NodeDef missing attrs 'seed2', 'seed' from}}
  %cst = "tf.Const"() {value = dense<[32, 12, 12, 64]> : tensor<4xi32>} : () -> tensor<4xi32>
  %0 = "tf.RandomUniform"(%cst) {} : (tensor<4xi32>) -> tensor<32x12x12x64xf32>
  // CHECK: return %1 : tensor<32x12x12x64xf32>
  func.return %0 : tensor<32x12x12x64xf32>
}
}
// -----

        %cst = "tf.Const"() {value = dense<2.000000e+00> : tensor<2x3x3x1xf32>} : () -> tensor<2x3x3x1xf32>
        %cst_0 = "tf.Const"() {value = dense<0.400000e+00> : tensor<3xf32>} : () -> tensor<3xf32>
        %cst_1 = "tf.Const"() {value = dense<0.500000e+00> : tensor<3xf32>} : () -> tensor<3xf32>
        %w = "tf.Mul"(%cst, %arg1) : (tensor<2x3x3x1xf32>, tensor<f32>) -> tensor<2x3x3x1xf32>

            "class OnField { @B(A.CST) String foo; }",
            "class OnParameter { void foo(@B(A.CST) int x) {} }",
            "class InMethodBody { void foo(int x) { @B(A.CST) int value = 5; } }",
        ]

        impl.snapshot { run("impl:${language.compileTaskName}") }

        when:
        source api: ["class A { public static final int CST = 1234; }"]
        run("impl:${language.compileTaskName}")

    %cst = "tfl.no_value"() {value = unit} : () -> none

  func.return %value0, %value1 : tensor<i64>, tensor<i64>
}

func.func private @else_branch(%arg0: tensor<i1>) -> tensor<i1> {
  %cst = "tf.Const"() {value = dense<"Empty SparseTensor with shape"> : tensor<!tf_type.string>} : () -> tensor<!tf_type.string>
  "tf.Assert"(%arg0, %cst) {device = "/job:localhost/replica:0/task:0/device:CPU:0", summarize = 3 : i64} : (tensor<i1>, tensor<!tf_type.string>) -> ()
  func.return %arg0 : tensor<i1>
}

    %cst = arith.constant dense<0.000000e+00> : tensor<5xf32>
    %cst_0 = arith.constant dense<1.0> : tensor<5x3xf32>
    %0 = "tfl.fully_connected"(%arg0, %cst_0, %cst) {fused_activation_function = "NONE", keep_num_dims = false, weights_format = "DEFAULT"} : (tensor<?x3xf32>, tensor<5x3xf32>, tensor<5xf32>) -> tensor<?x5xf32>

module {
  func.func @conv_with_int_per_layer(%arg0: tensor<*xf32>) -> tensor<*xf32> {
    %cst = "tf.Const"() {device = "", value = dense<[-0.0192535277, -5.998660e-03]> : tensor<2xf32>} : () -> tensor<2xf32>

			if sd[i].Load() == 0 {
				continue
			}
			// We found a function that was executed.
			nCtrs := sd[i+coverage.NumCtrsOffset].Load()
			cst := i + coverage.FirstCtrOffset

			if cst+int(nCtrs) > len(sd) {
				break
			}
			counters := sd[cst : cst+int(nCtrs)]
			for i := range counters {
				if counters[i].Load() != 0 {
					totExec++
				}
			}
			i += coverage.FirstCtrOffset + int(nCtrs) - 1

    %cst = "tfl.no_value"() {value = unit} : () -> none