}

// DEBUG-LABEL: QuantizeSplit
func.func @QuantizeSplit(%arg: tensor<4x!quant.uniform<u8:f32, 1.0>>, %cst: tensor<i32>) -> (tensor<2x!quant.uniform<u8:f32, 1.0>>,tensor<2x!quant.uniform<u8:f32, 1.0>>) {
  %0 = "tfl.dequantize"(%arg) : (tensor<4x!quant.uniform<u8:f32, 1.0>>) -> tensor<4xf32>
  %1:2 = "tfl.split"(%cst, %0) {num_splits = 2 : i32} : (tensor<i32>, tensor<4xf32>) -> (tensor<2xf32>, tensor<2xf32>)

  // CHECK: return
}

// -----
// This test verifies that the extraction works correctly for evaluation-only models.
module {
  func.func @main() {
    %cst = "tf.Const"() {value = dense<2> : tensor<i32>} : () -> tensor<i32>
    %0 = "tf.While"(%cst) {body = @while_body, cond = @while_cond, is_stateless = false} : (tensor<i32>) -> (tensor<i32>)
    return
  }
  func.func private @while_body(%arg0: tensor<i32>) -> (tensor<i32>) {

  // CHECK: %[[CST:.*]] = "tf.Const"() <{value = dense<0> : tensor<i32>}> : () -> tensor<i32>
  // CHECK: %[[RES0:.*]] = "tf.GatherV2"(%arg0, %arg2, %cst) <{batch_dims = 0 : i64}> {device = "/job:localhost/replica:0/task:0/device:GPU:0"} : (tensor<1600x1x32xf32>, tensor<i32>, tensor<i32>) -> tensor<1x32xf32>

module attributes {tf.versions = {producer = 888 : i32}} {
func.func @testReshapeShapeInference(%arg0: tensor<3x4xi32>) -> tensor<*xi32> {
  %cst = arith.constant dense<[1, 6, 2]> : tensor<3xi32>
  // CHECK: "tfl.reshape"(%arg0, %cst) : (tensor<3x4xi32>, tensor<3xi32>) -> tensor<1x6x2xi32>
  %0 = "tfl.reshape"(%arg0, %cst) : (tensor<3x4xi32>, tensor<3xi32>) -> tensor<*xi32>
  func.return %0 : tensor<*xi32>
}
}

// -----

}
func.func private @XlaCallModule_aten.avg_pool2d.default.impl_2(%arg0: tensor<1x1x1x8xf32>) -> tensor<1x1x1x4xf32>

// CHECK-LABEL: avg_pool2d_3
// CHECK: %cst = arith.constant dense<[0, 2, 3, 1]> : tensor<4xi32>
// CHECK: %0 = "tfl.transpose"(%arg0, %cst) : (tensor<1x1x1x8xf32>, tensor<4xi32>) -> tensor<1x1x8x1xf32>
// CHECK{LITERAL}: %cst_0 = arith.constant dense<[[0, 0], [0, 0], [1, 1], [0, 0]]> : tensor<4x2xi32>

  %cst = "tf.Const"() {value = dense<5.000000e+00> : tensor<f32>} : () -> tensor<f32>
  %0 = "tf.TPUCompilationResult"() {_tpu_compilation_status = "cluster_tpu_function", device = ""} : () -> tensor<!tf_type.string>

			GotConn: func(info httptrace.GotConnInfo) {
				rt.conn = info.Conn.(*transportDialTesterConn)
			},
		})
		req, _ := http.NewRequestWithContext(ctx, "POST", dt.cst.ts.URL, pr)
		req.Header.Set("Content-Type", "text/plain")
		rt.res, rt.err = dt.cst.tr.RoundTrip(req)
		dt.t.Logf("RoundTrip %v: done (err:%v)", rt.roundTripID, rt.err)
		close(rt.done)
	}()
	return rt
}

  %0 = arith.constant 3.0: f32
  %t = tfr.constant f32 -> !tfr.attr
  %cst = tfr.call @tf__const(%0, %t) : (f32, !tfr.attr) -> !tfr.tensor
  %4 = "tfr.cast"(%cst) : (!tfr.tensor) -> tensor<f32>
  func.return %4 : tensor<f32>

// CHECK: %[[cst:.*]] = "tf.Const"() <{value = dense<3.000000e+00> : tensor<f32>}> : () -> tensor<f32>
// CHECK: return %[[cst]] : tensor<f32>
}

// CHECK-LABEL: attribute_propagate

  %cst = "tf.Const"() {__op_key = 2: i32, device = "/device:CPU:0", value = dense<0> : tensor<i32>} : () -> tensor<i32>
  // Predicate should be inlined.
  // CHECK-NEXT: tf_mlrt.predicate
  // CHECK-NEXT: mlrt.cond
  // CHECK-NEXT: tf_mlrt.predicate

  // CHECK-NEXT: mlrt.while

  %index = arith.constant 0 : index
  %cst = arith.constant 1 : i64
  %eq = arith.cmpi "eq", %n, %cst : i64
  %v1 = tfr.get_element %values[%index] : (!tfr.tensor_list, index) -> !tfr.tensor
  %res = scf.if %eq -> !tfr.tensor {
    scf.yield %v1 : !tfr.tensor
  } else {
    %step = arith.index_cast %cst : i64 to index
    %end = arith.index_cast %n : i64 to index