%cst_0 = arith.constant dense<[2, -1]> : tensor<2xi64>
  %0 = "tf.FakeQuantWithMinMaxVars"(%arg0, %arg1, %arg2) {num_bits = 3, narrow_range = false} : (tensor<1x2xf32>, tensor<f32>, tensor<f32>) -> tensor<1x2xf32>
  %1 = "tf.Reshape"(%0, %cst_0) : (tensor<1x2xf32>, tensor<2xi64>) -> tensor<2x1xf32>
  %2 = "tf.Reshape"(%0, %cst_0) : (tensor<1x2xf32>, tensor<2xi64>) -> tensor<2x1xf32>
  func.return %1, %2 : tensor<2x1xf32>, tensor<2x1xf32>

func.func @main(%arg0: tensor<10x8x9xf32>) -> tensor<2xi64> {
  %elem_shape = "tf.Const"() {value = dense<[8, 9]> : tensor<2xi32>} : () -> tensor<2xi32>
  %tl = "tf.TensorListFromTensor"(%arg0, %elem_shape) : (tensor<10x8x9xf32>, tensor<2xi32>) -> tensor<!tf_type.variant<tensor<8x9xf32>>>
  // CHECK: %[[SHAPE:.*]] = "tf.Const"() <{value = dense<[8, 9]> : tensor<2xi64>}> : () -> tensor<2xi64>

      stablehlo.return %3 : tensor<f32>
  }) {
    window_dimensions = array<i64: 1, 1, 2, 2>,
    window_strides = array<i64: 1, 1, 2, 2>,
    padding = dense<[[0, 0], [0, 0], [1, 1], [1, 1]]> : tensor<4x2xi64>
  } : (tensor<1x4x16x16xf32>, tensor<f32>) -> tensor<1x4x9x9xf32>
  return %2 : tensor<1x4x9x9xf32>
}
// CHECK-SAME: %[[ARG:.+]]: tensor<1x16x16x4xf32>
// CHECK-DAG: stablehlo.constant

// CHECK:  %[[cst:.*]] = "tfl.pseudo_qconst"() <{qtype = tensor<2x4x!quant.uniform<i4:f32, 2.500000e-01:-1>>, value = dense<{{\[\[}}-4, -3, -2, -1{{\]}}, [0, 1, 2, 3{{\]\]}}> : tensor<2x4xi4>}>
// CHECK:  return %[[cst]]
}

// CHECK-LABEL: QuantizeDenseFloatConst
func.func @QuantizeDenseFloatConst() -> tensor<2x2x!quant.uniform<u8:f32, 7.8431372549019615E-4:128>> {