src.flat<tstring>()(i) = data[i];
    }
    TF_Tensor* dst = TF_TensorFromTensor(src, &status);
    ASSERT_TRUE(status.ok()) << status.message();

    // Convert back to a C++ Tensor and ensure we get expected output.
    Tensor output;
    ASSERT_EQ(absl::OkStatus(), TF_TensorToTensor(dst, &output)) << line;
    ASSERT_EQ(src.NumElements(), output.NumElements()) << line;

  }

  tensorflow::shape_inference::ShapeHandle shape = ic->output(output.index);

  int rank = -1;
  if (ic->RankKnown(shape)) {
    rank = ic->Rank(shape);
  }

  if (num_dims != rank) {
    status->status = InvalidArgument("Expected rank is ", num_dims,
                                     " but actual rank is ", rank);
    return;
  }

  if (num_dims == 0) {
    // Output shape is a scalar.

        as `tf.tpu.experimental.embedding.serving_embedding_lookup` which can
        take arbitrary rank of dense and sparse tensor. For ragged tensor,
        though the input tensor remains to be rank 2, the activations now can be
        rank 2 or above by specifying the output shape in the feature config or
        via the build method.

*   Add

  const auto& shape = reader->GetVariableToShapeMap().at(name);
  int rank = shape.dims();
  if (num_dims != rank) {
    status->status = InvalidArgument("Expected rank is ", num_dims,
                                     " but actual rank is ", rank);
    return;
  }
  for (int i = 0; i < num_dims; i++) {
    dims[i] = shape.dim_size(i);
  }
}

    for (int i = 0; i < noutputs; ++i) {
      output_names_vec.push_back(string(output_names[i]));
    }
  }

  // Process control output names.
  std::vector<string> control_output_names_vec;
  if (control_output_names) {
    control_output_names_vec.reserve(ncontrol_outputs);
    for (int i = 0; i < ncontrol_outputs; ++i) {
      control_output_names_vec.push_back(string(control_output_names[i]));
    }
  }