// CHECK-LABEL: @qu8
  // CHECK: <{qtype = tensor<3x!quant.uniform<u8<1:255>:f32, 1.000000e+00>>, value = dense<1> : tensor<3xi8>}> : () -> tensor<3x!quant.uniform<u8<1:255>:f32, 1.000000e+00>>
  %0 = "tfl.pseudo_qconst"() { qtype = tensor<3x!quant.uniform<u8<1:255>:f32, 1.0>>, value = dense<1> : tensor<3xi8>} : () -> tensor<3x!quant.uniform<u8<1:255>:f32, 1.0>>
  func.return %0 : tensor<3x!quant.uniform<u8<1:255>:f32, 1.0>>
}

// CHECK-NEXT:      %20 = mhlo.dynamic_reshape %2, %19 : (tensor<2x3x4x?xf32>, tensor<3xi32>) -> tensor<2x3x?xf32>
// CHECK-NEXT:      %21 = "tfl.batch_matmul"(%11, %20) <{adj_x = false, adj_y = false, asymmetric_quantize_inputs = false}> : (tensor<2x?x3xf32>, tensor<2x3x?xf32>) -> tensor<2x?x?xf32>
// CHECK-NEXT:      %22 = "tfl.shape"(%arg0) : (tensor<2x2x?x3xf32>) -> tensor<4xi32>

func.func @cast_i1_to_i8() -> tensor<2xi8> {
  %cst = arith.constant dense<[false, true]> : tensor<2xi1>
  %0 = "tfl.cast"(%cst) : (tensor<2xi1>) -> tensor<2xi8>
  func.return %0 : tensor<2xi8>

// CHECK: %[[CST:.*]] = arith.constant dense<[0, 1]> : tensor<2xi8>
// CHECK:  return %[[CST]]
}

// CHECK-LABEL: @cast_i1_to_ui8
func.func @cast_i1_to_ui8() -> tensor<2xui8> {