dimension_numbers = #mhlo.conv<[b, 0, f]x[0, i, o]->[b, 0, f]>,
    feature_group_count = 1 : i64,
    lhs_dilation = dense<1> : tensor<1xi64>,
    padding = dense<0> : tensor<1x2xi64>,
    precision_config = [#mhlo<precision DEFAULT>, #mhlo<precision DEFAULT>],
    rhs_dilation = dense<1> : tensor<1xi64>,
    window_strides = dense<1> : tensor<1xi64>

    // Compute ConvDimensionNumbers, dilation, and padding.
    SmallVector<int64_t, num_spatial_dims> spatial_dims;
    SmallVector<int64_t, num_spatial_dims> lhs_dilation;
    SmallVector<int64_t, num_spatial_dims> rhs_dilation;
    SmallVector<int64_t, num_spatial_dims * 2> paddings;

    for (int i : llvm::seq<int>(0, num_spatial_dims)) {
      const int64_t spatial_dim =

  %feature_group_count = "tf.Const"() {value = dense<1> : tensor<i32>} : () -> tensor<i32>
  %lhs_dilation = "tf.Const"() {value = dense<[4, 1, 1]> : tensor<3xi32>} : () -> tensor<3xi32>
  %rhs_dilation = "tf.Const"() {value = dense<1> : tensor<3xi32>} : () -> tensor<3xi32>
  %padding = "tf.Const"() {value = dense<0> : tensor<3x2xi32>} : () -> tensor<3x2xi32>