}

  absl::Status Shape(tensorflow::PartialTensorShape* shape) const override {
    DCHECK(shape != nullptr);
    TF_Status status;
    int num_dims = TF_GraphGetTensorNumDims(graph_, output_, &status);
    DCHECK_GE(num_dims, -1);
    TF_RETURN_IF_ERROR(StatusFromTF_Status(&status));
    if (num_dims == kUnknownRank) {
      return absl::OkStatus();
    }

  num_dims = TF_GraphGetTensorNumDims(graph, feed_out_0, s);
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
  EXPECT_EQ(-1, num_dims);

  // Set the shape to be unknown, expect no change.
  TF_GraphSetTensorShape(graph, feed_out_0, /*dims=*/nullptr, -1, s);
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
  num_dims = TF_GraphGetTensorNumDims(graph, feed_out_0, s);

// in `graph`.
//
// If the number of dimensions in the shape is unknown, returns -1.
//
// Returns an error into `status` if:
//   * `output` is not in `graph`.
TF_CAPI_EXPORT extern int TF_GraphGetTensorNumDims(TF_Graph* graph,
                                                   TF_Output output,
                                                   TF_Status* status);

  }
  tensorflow::shape_inference::ShapeHandle new_shape =
      tensorflow::ShapeHandleFromDims(ic, num_dims, dims);
  status->status = graph->refiner.SetShape(node, output.index, new_shape);
}

int TF_GraphGetTensorNumDims(TF_Graph* graph, TF_Output output,
                             TF_Status* status) {
  Node* node = &output.oper->node;

  mutex_lock l(graph->mu);
  tensorflow::shape_inference::InferenceContext* ic =

    promoted the non-batch version of the ops to handle batches of matrices.
*   Tracing/timeline support for distributed runtime (no GPU profiler yet).
*   C API gives access to inferred shapes with `TF_GraphGetTensorNumDims` and
    `TF_GraphGetTensorShape`.
*   Shape functions for core ops have moved to C++ via
    `REGISTER_OP(...).SetShapeFn(...)`. Python shape inference RegisterShape