func.return %1 : tensor<1x2xf32>

// CHECK-DAG:  %[[CONSTANT:.*]] = "tfl.pseudo_const"(){{.*}}dense<{{\[\[}}1.000000e+00, 2.000000e+00, 3.000000e+00], [2.000000e+00, 4.000000e+00, 6.000000e+00]]> : tensor<2x3xf32>
// CHECK-DAG:  %[[CONSTANT0:.*]] = "tfl.pseudo_const"(){{.*}}dense<[2.000000e+00, 4.000000e+00]> : tensor<2xf32>

	}{
		{
			name:         "openmetrics accept header",
			acceptHeader: `application/openmetrics-text; version=0.0.1,text/plain;version=0.0.4;q=0.5,*/*;q=0.1`,
		},
		{
			name:         "openmetrics v1 accept header",
			acceptHeader: `application/openmetrics-text; version=1.0.0,text/plain;version=0.0.4;q=0.5,*/*;q=0.1`,
		},
		{
			name:         "plaintext accept header",
			acceptHeader: string(FmtText),
		},
		{

        x=ops.convert_to_tensor(input_data)
    )
    # Tests that the quantized graph outputs similar values. The rtol and atol
    # values are arbitrary.
    self.assertAllClose(new_outputs, expected_outputs, rtol=0.02, atol=0.04)

    # Due to other meta data, the compression is not exactly 1/4.
    self.assertLess(
        testing.get_size_ratio(
            self._output_saved_model_path, self._input_saved_model_path
        ),

// CHECK{LITERAL}: %cst_1 = arith.constant dense<[[[[1.000000e+00], [2.000000e+00]], [[2.000000e+00], [4.000000e+00]]]]> : tensor<1x2x2x1xf32>
// CHECK: %3 = tfl.mul %2, %cst_1 {fused_activation_function = "NONE"} : tensor<1x2x2x1xf32>
// CHECK: %cst_2 = arith.constant dense<[0, 3, 1, 2]> : tensor<4xi32>

    %4 = stablehlo.add %3, %2 : tensor<1x4x4x4x!quant.uniform<i32:f32:3, {6.000000e+00, 6.000000e+00, 6.000000e+00, 6.000000e+00}>>

		// accumulating at approximately the same time from the set of nodes due to priority and
		// fairness effect.
		go wait.JitterUntil(kl.syncNodeStatus, kl.nodeStatusUpdateFrequency, 0.04, true, wait.NeverStop)
		go kl.fastStatusUpdateOnce()

		// start syncing lease
		go kl.nodeLeaseController.Run(context.Background())
	}
	go wait.Until(kl.updateRuntimeUp, 5*time.Second, wait.NeverStop)

// CHECK: }
func.func @convert_floor_div_broadcast_cst(%arg0: tensor<10x8xf32>) -> tensor<10x8xf32> {
  %0 = mhlo.constant dense<1.000000e+00> : tensor<10x8xf32>
  %1 = mhlo.constant dense<[1.000000e+00, 2.000000e+00, 4.000000e+00, 8.000000e+00, 1.600000e+01, 3.200000e+01, 6.400000e+01, 1.280000e+02]> : tensor<8xf32>
  %2 = mhlo.constant dense<0.000000e+00> : tensor<10x8xf32>
  %3 = mhlo.constant dense<-1.000000e+00> : tensor<10x8xf32>