// So result shape:
  // Dim #0: begin mask (1) -> begin = 0; end 8 canonicalized to 4: so 4
  // Dim #1: 4 to 65 stride 4: so 16
  // Dim #2: begin -3 + 1024 = 1021; end mask (1) -> end = -1: so 1022
  // result shape: [4, 16, 1022]

  %begin = "tf.Const"() {value = dense<[1, 4, -3]> : tensor<3xi32>} : () -> (tensor<3xi32>)
  %end = "tf.Const"() {value = dense<[8, 65, 42]> : tensor<3xi32>} : () -> (tensor<3xi32>)

  %0 = "mhlo.convolution"(%arg0, %arg1) {batch_group_count = 1 : i64,
    dimension_numbers = #mhlo.conv<[b, 0, 1, f]x[0, 1, o, i]->[b, 0, 1, f]>, feature_group_count = 1 : i64, lhs_dilation = dense<2> : tensor<2xi64>, padding = dense<[[2, 2], [2, 2]]> : tensor<2x2xi64>,
    precision_config = [#mhlo<precision DEFAULT>, #mhlo<precision DEFAULT>], rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>

  %201 = "tf.ConcatV2"(%9, %200, %9, %14) {device = ""} : (tensor<0xi32>, tensor<1xi32>, tensor<0xi32>, tensor<i32>) -> tensor<1xi32>
  %202 = "tf.Reshape"(%198, %201) {device = ""} : (tensor<2x?xi64>, tensor<1xi32>) -> tensor<?xi64>
  %203 = "tf.Shape"(%202) {device = ""} : (tensor<?xi64>) -> tensor<1xi64>