TF_DeleteExecutionContext(ctx);
  return wrap(func);
}

TF_AbstractTensor* TF_AddFunctionParameter(TF_ExecutionContext* func,
                                           TF_DataType dtype, TF_Shape shape,
                                           TF_Status* s) {
  DCHECK_GE(shape.num_dims, -1);
  TracingTensorHandle* t;
  TracingContext* tracing_ctx = dyn_cast<TracingContext>(unwrap(func));
  if (!tracing_ctx) {

  typedef VariantSubTypeBuilder Builder;
  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
    VT_SHAPE = 4,
    VT_TYPE = 6,
    VT_HAS_RANK = 8
  };
  const ::flatbuffers::Vector<int32_t> *shape() const {
    return GetPointer<const ::flatbuffers::Vector<int32_t> *>(VT_SHAPE);
  }
  tflite::TensorType type() const {
    return static_cast<tflite::TensorType>(GetField<int8_t>(VT_TYPE, 0));

    Value result = reduction.getResult(0);

    // The mean op needs to divide by the product of the reduced dimensions.
    if (std::is_same<OpTy, TF::MeanOp>::value) {
      Value in_shape = rewriter.create<shape::ShapeOfOp>(loc, op.getInput());
      Value divisor_count = rewriter.create<arith::ConstantIndexOp>(loc, 1);
      for (size_t i = 0; i < input_shape.size(); ++i) {

    }
    return
  }
```
### `-tf-rewrite-tpu-embedding-ops`

_Rewrites TPU embedding send/recv ops by adding TPU embedding deduplication data_

### `-tf-shape-inference`

_Shape inference on TF dialect and ops implementing InferTypeOpInterface_

Fixed point shape refinement pass that utilizes the shape functions
registered on ops using the InferTypeOpInterface as well as by bridging to

    Arg<TF_Int64Tensor, [{2-D.  The `indices` of the second `SparseTensor`, size `[nnz, ndims]` Matrix.}]>:$b_indices,
    Arg<TF_NumberTensor, [{1-D.  The `values` of the second `SparseTensor`, size `[nnz]` Vector.}]>:$b_values,
    Arg<TF_Int64Tensor, [{1-D.  The `shape` of the second `SparseTensor`, size `[ndims]` Vector.}]>:$b_shape,