switch (attr->value_case()) {
#define SINGLE_CASE(kK, attr_type, size_expr) \
  case tensorflow::AttrValue::kK:             \
    metadata.is_list = 0;                     \
    metadata.list_size = -1;                  \
    metadata.type = attr_type;                \
    metadata.total_size = size_expr;          \
    break;

    SINGLE_CASE(kS, TF_ATTR_STRING, attr->s().length());

  explicit ExpGradientFunction(AbstractTensorHandle* exp) : exp_(exp) {
    exp->Ref();
  }
  Status Compute(AbstractContext* ctx,
                 absl::Span<AbstractTensorHandle* const> grad_outputs,
                 absl::Span<AbstractTensorHandle*> grad_inputs) override {
    AbstractTensorHandle* conj_output;
    std::string name = "Conj_Exp_Grad";
    TF_RETURN_IF_ERROR(SafeConj(ctx, exp_.get(), &conj_output, name.c_str()));

        tf.math.invert_permutation.
    *   Add `tf.math.sobol_sample` op.
    *   Add `tf.math.xlog1py`.
    *   Add `tf.math.special.{dawsn,expi,fresnel_cos,fresnel_sin,spence}`.
    *   Add a Modified Discrete Cosine Transform (MDCT) and its inverse to
        `tf.signal`.
*   TPU Enhancements:

  std::unique_ptr<GradientFunction> gradient_function(
      new PassThroughGradientFunction);
  tape.RecordOperation(inputs, {exp_output}, gradient_function.release());
  TF_RETURN_IF_ERROR(tape.ComputeGradient(ctx,
                                          /*targets*/ {exp_output},
                                          /*sources=*/inputs,
                                          /*output_gradients=*/{},