}
	}

	if cst.ts.Config.ErrorLog == nil {
		cst.ts.Config.ErrorLog = log.New(testLogWriter{t}, "", 0)
	}

	switch mode {
	case http1Mode:
		cst.ts.Start()
	case https1Mode:
		cst.ts.StartTLS()
	case http2Mode:
		ExportHttp2ConfigureServer(cst.ts.Config, nil)
		cst.ts.TLS = cst.ts.Config.TLSConfig
		cst.ts.StartTLS()
	default:
		t.Fatalf("unknown test mode %v", mode)
	}
	cst.c = cst.ts.Client()

  func.return %2 : tensor<256x8x7x16xf32>

// CHECK-DAG: %[[cst:.*]] = "tf.Const{{.*}} dense<8.000000e+00> : tensor<3x3x3x16xf32>
// CHECK-DAG: %[[cst_0:.*]] = "tf.Const{{.*}} dense<1.200000e+01> : tensor<16xf32>
// CHECK-NEXT: %[[conv:.*]] = "tf.Conv2D"(%arg0, %[[cst]])
// CHECK-NEXT: %[[bias:.*]] = "tf.AddV2"(%[[conv]], %[[cst_0]])
// CHECK-NEXT: return %[[bias]] : tensor<256x8x7x16xf32>
}

    %cst_0 = "tf.Const"() {value = dense<1.000000e+00> : tensor<2xf32>} : () -> tensor<2xf32>
    %var_0 = "tf.VarHandleOp"() {shared_name = "var_0"} : () -> tensor<!tf_type.resource<tensor<2xf32>>>
    "tf.AssignVariableOp"(%var_0, %cst_0) : (tensor<!tf_type.resource<tensor<2xf32>>>, tensor<2xf32>) -> ()
    %add_0 = "tf.Identity"(%cst_0) : (tensor<2xf32>) -> tensor<2xf32>

  %cst = arith.constant dense<1.000000e+00> : tensor<256x256xf32>
  %cst_0 = arith.constant dense<0> : tensor<i32>
  %cst_1 = arith.constant dense<-1> : tensor<i32>

    %cst = "tf.Const"() {device = "", value = dense<[7.72826624, 8.8264122, 3.64885974]> : tensor<3xf32>} : () -> tensor<3xf32>

  %cst = arith.constant dense<false> : tensor<i1>
  func.return %cst : tensor<i1>
}

func.func private @_functionalize_if_then_branch_00(%arg0: tensor<*xi1>, %arg1: tensor<*xf32>, %arg2: tensor<*xf32>) -> tensor<i1>  {
  %cst = arith.constant dense<true> : tensor<i1>
  func.return %cst : tensor<i1>
}
// CHECK-NOT: tf.If
// CHECK: return

// -----

  func.return %0 : tensor<*xf32>
}

// CHECK: func @xla_gather
// CHECK-DAG: %[[cst:.*]] = "tf.Const"() <{value = dense<0> : tensor<2xi64>}> : () -> tensor<2xi64>
// CHECK-DAG: %[[cst_0:.*]] = "tf.Const"() <{value = dense<1> : tensor<1x1xi64>}> : () -> tensor<1x1xi64>
// CHECK-DAG: %[[cst_1:.*]] = "tf.Const"() <{value = dense<-1> : tensor<1xi64>}> : () -> tensor<1xi64>

    %cst = "tf.Const"() {device = "", value = dense<1.0> : tensor<2x3x3x2xf32>} : () -> tensor<2x3x3x2xf32>
    %cst_0 = "tf.Const"() {value = dense<0.000000e+00> : tensor<2xf32>} : () -> tensor<2xf32>
    %0 = "tf.Cast"(%arg0) {Truncate = false, device = ""} : (tensor<1x3x4x3xf32>) -> tensor<1x3x4x3xf32>

  %cst_0 = arith.constant dense<[2, -1]> : tensor<2xi64>
  %0 = "tf.FakeQuantWithMinMaxVars"(%arg0, %arg1, %arg2) {num_bits = 5, narrow_range = false} : (tensor<1x2xf32>, tensor<f32>, tensor<f32>) -> tensor<1x2xf32>
  %1 = "tf.Reshape"(%0, %cst_0) : (tensor<1x2xf32>, tensor<2xi64>) -> tensor<2x1xf32>
  func.return %1 : tensor<2x1xf32>

// CHECK:  %cst = arith.constant
// CHECK:  %0 = "tf.Reshape"(%arg0, %cst)

    %cst = arith.constant dense<0.000000e+00> : tensor<5xf32>
    %cst_0 = arith.constant dense<1.0> : tensor<5x384xf32>
    %cst_1 = arith.constant dense<1.0> : tensor<5x384xf32>