if (!shaped_type) {
    return errors::Internal("Constant doesn't have a shape");
  }
  auto op = builder.create<tfl::ExternalConstOp>(
      loc, shaped_type, builder.getI32IntegerAttr(buffer_index));
  return op.getOperation();
}

// TODO(b/172664358): Creates a new op instead of reusing constant op.
// Creates a constant op with "tfl.is_variable" attribute to represent stateful

  auto output_shape_kept_dims =
      ReducedShapeHelper(scope, input_shape, op.input(1));

  // This step "flips" any 1s with values from the input_shape, and
  // replaces remaining entries with 1. This creates a shape that
  // shows how much each dimension in the incoming gradient should be
  // replicated.
  // tile_scaling = [1, 3]
  auto tile_scaling = SafeDivHelper(scope, input_shape, output_shape_kept_dims);

    return a->isBeforeInBlock(b);
  });

  // Create tf_device::ClusterOp before the last operation in the block that
  // is a part of a match set.
  auto back = cluster.operations.back();
  auto loc = back->getLoc();
  OpBuilder builder(back);

  auto cluster_op =
      builder.create<tf_device::ClusterOp>(loc, return_types, policy);

  // Create block in cluster_op's region and move 'cluster.operations' into

    auto slice_size_attr = DenseIntElementsAttr::get(idx_type, slice_size_i32);
    auto slice_size =
        rewriter.create<arith::ConstantOp>(op.getLoc(), slice_size_attr);

    ArrayRef<int64_t> strides = op.getStrides();
    // If stride of every dimension is 1, create tfl.slice and return early.
    // Otherwise, create tfl.strided_slice instead.
    if (llvm::all_of(strides, [](int64_t stride) { return stride == 1; })) {

                                    tflite::BuiltinOperator builtin) {
  auto it = opcode_index_map_.insert({op_name, 0});

  // If the insert succeeded, the opcode has not been created already. Create a
  // new operator code and update its index value in the map.
  if (it.second) {
    it.first->second = opcodes_.size();
    auto custom_code = builtin == tflite::BuiltinOperator_CUSTOM

    is_list_output.push_back(is_list);
  }

  strings::StrAppend(&comment, "\nReturns:\n");
  if (graph_op_def.output_arg_size() == 0) {  // No outputs.
    strings::StrAppend(&comment, "* the created `Operation`\n");
  } else if (graph_op_def.output_arg_size() == 1) {  // One output
    if (is_list_output[0]) {
      strings::StrAppend(&comment, "* `OutputList`: ");
    } else {

  SliceInputs slice_inputs_int64 =
      MakeSliceIndexAndSizeInt64(host_scope, slice_inputs);

  // Create a list of all control dependencies to be copied when possibly
  // replacing nodes related to slice_size.
  Node* old_size;
  std::vector<const Edge*> old_size_ctrl_deps;
  TF_RETURN_IF_ERROR(slice->input_node(2, &old_size));

  Value tensor_i32 = builder.create<TF::CastOp>(
      loc, tensor_type.clone(builder.getIntegerType(32)), tensor);
  auto reduced =
      builder.create<TF::SumOp>(loc, tensor_i32, reduction_indices_value,
                                /*keep_dims=*/builder.getBoolAttr(true));
  auto mul_op = builder.create<TF::MulOp>(loc, zp, reduced);

  // Add i32 -> i8 requantization.
  UniformQuantizeOp uniform_quant_op = rewriter.create<UniformQuantizeOp>(
      op.getLoc(), func_result_type, op.getResults());
  cast<func::ReturnOp>(entry_func_op.getBody().front().getTerminator())
      .setOperand(0, uniform_quant_op);
}

template <typename GemmStyleOp>
// Creates a quantized bias pattern for static and dynamic shape case

// be created).
static void DumpModule(mlir::ModuleOp module, std::string file_prefix) {
  std::string prefix = GetDumpDirFromEnvVar();
  if (prefix.empty()) return;

  auto* env = tensorflow::Env::Default();
  auto status = env->RecursivelyCreateDir(prefix);
  if (!status.ok()) {
    LOG(WARNING) << "cannot create directory '" << prefix
                 << "': " << status.message();