%0 = "tf.Const"() {value = dense<1> : tensor<i32>} : () -> tensor<i32>
  %1 = "tf.ExpandDims"(%arg1, %0) : (tensor<*xi32>, tensor<i32>) -> tensor<*xi32>
  %2 = "tf.Const"() {value = dense<1> : tensor<i32>} : () -> tensor<i32>
  %3 = "tf.Const"() {value = dense<4096> : tensor<i32>} : () -> tensor<i32>
  %4 = "tf.Const"() {value = dense<0> : tensor<i32>} : () -> tensor<i32>
  %5 = "tf.Range"(%4, %3, %2) : (tensor<i32>, tensor<i32>, tensor<i32>) -> tensor<4096xi32>

        // expected-remark@above {{ID: 0}}
        // expected-remark@above {{Successors: {1}}}
        %1 = "tf.CollectiveReduceV2"(%input, %group_size, %group_key, %instance_key) {merge_op = "Mul", final_op = "Id"} : (tensor<f32>, tensor<i32>, tensor<i32>, tensor<i32>) -> tensor<f32>
        // expected-remark@above {{ID: 1}}

    ArrayRef<int64_t> shape(rank);
    TensorType permutation_type =
        operand_type.cloneWith(shape, rewriter.getI32Type());
    // Cast permutation attribute from i64 to i32 as they are required to be i32
    // in TFLite.
    SmallVector<int32_t> permutation_i32 =
        CastI64ArrayToI32(op.getPermutation()).value();
    auto permutation_attr =

// static shape and activation function is none. For example:
//
//   // %input: tensor<1x3x2xf32>
//   %unpack:3 = "tfl.unpack"(%input) {axis = 1 : i32, num = 3 : i32}
//   %res = "tfl.concatenation"(%unpack#0, %unpack#1, %unpack#2)
//        {axis = -1 : i32, fused_activation_function = "NONE"}
//
// can be optimized to
//
//   %cst = arith.constant dense<[1, 6]> : tensor<2xi32>

        input_type.getNumElements() != output_type.getNumElements()) {
      return failure();
    }
    // Reshape op supports only new shape as I32. Add a cast op to I32 always
    // to make sure the introduced Reshape Op is a valid one.
    auto result_type = tensorflow::GetTypeFromTFTensorShape(
        op.getShape().getType().cast<RankedTensorType>().getShape(),

            .getElementType();

    // The mhlo dynamic_slice's start_indices can be either signed/unsigned
    // int32/int64. However, TF only takes in either i32 or i64 types for begin,
    // so we will always put a cast.
    Type signed_start_indices_element_type;
    if (start_indices_element_type.isInteger(32)) {
      signed_start_indices_element_type = rewriter.getI32Type();

        mlir::cast<mlir::TensorType>(input_type).getElementType();
    RankedTensorType type = tensorflow::GetTypeFromTFTensorShape(
        {static_cast<int64_t>(shape.size())}, element_type);
    // Input could only be i32 or i64. For i32, downcast to int32_t array.
    if (element_type.isInteger(32)) {
      SmallVector<int32_t, 4> i32_shape;
      for (auto s : shape) i32_shape.push_back(static_cast<int32_t>(s));