inputs={'input_tensor': meta_graph_pb2.TensorInfo(name='input:0')}
    )

    with self.session():
      input_tensor = constant_op.constant([1, 2, 3, 4, 5, 6])
      sample = {'input_tensor': input_tensor}

      feed_dict = repr_dataset.create_feed_dict_from_input_data(
          sample, signature_def
      )
      input_tensor_data = input_tensor.eval()

    self.assertLen(feed_dict, 1)

  return ElementsAttr(DenseStringElementsAttr::get(type, string_refs));
}

absl::StatusOr<ElementsAttr> ConvertTensor(const Tensor& input_tensor,
                                           Builder* builder) {
  const auto& input_dtype = input_tensor.dtype();
  const auto& input_shape = input_tensor.shape();
  Type elt_type;
  TF_RETURN_IF_ERROR(ConvertDataType(input_dtype, *builder, &elt_type));
  SmallVector<int64_t, 4> shape;

        pass

      @def_function.function
      def add(self, input_tensor: core.Tensor) -> Mapping[str, core.Tensor]:
        """Performs an add operation.

        Args:
          input_tensor: Input tensor to perform add on.

        Returns:
          A map of: output key -> output result.
        """
        out = math_ops.add(input_tensor, input_tensor)
        return {'output': out}

    model = AddModel()

      // Write the value from `input_tensor` if it is the last axis or
      // recurse into the next axis.
      const bool is_last_axis = output_axis == num_dimensions - 1;
      if (is_last_axis) {
        new_values->push_back(
            input_tensor.getValues<Attribute>()[*input_indices]);
      } else {
        ComputePermutation(input_tensor, perm, output_shape, num_dimensions,

  // CHECK: %[[CONSTANT_1:.*]] = stablehlo.constant dense<1.000000e+03> : tensor<1x64xf32>
  // CHECK: return
  // CHECK: }

    module attributes {tf_saved_model.semantics} {
      func.func @main(%arg: tensor<1x2xf32> {tf_saved_model.index_path = ["input_tensor:0"]}) -> (tensor<1x2xf32> {tf_saved_model.index_path = ["output_tensor:0"]}) attributes {tf.entry_function = {inputs = "input_tensor:0", outputs = "output_tensor:0"}, tf_saved_model.exported_names = ["main"]} {
        %0 = tf_executor.graph {
          tf_executor.fetch %arg : tensor<1x2xf32>

      return %2 : tensor<1x3xf32>
    }
  }
)mlir";

constexpr absl::string_view kModuleXlaCallModule = R"mlir(
  module {
    func.func @main(%arg0: tensor<?x2xf32> {tf_saved_model.index_path = ["input_tensor"]}) -> (tensor<?x2xf32>) {
      %0 = stablehlo.constant dense<[-0.211145893, -0.708605706]> : tensor<2xf32>
      %1 = stablehlo.constant dense<[[-0.630731344, 0.54962182], [0.180364341, -0.764542698]]> : tensor<2x2xf32>

}
constexpr absl::string_view kModuleDotWeightOnlyPtq = R"mlir(
  module {
    func.func @main(%arg0: tensor<?x2xf32> {tf_saved_model.index_path = ["input_tensor"]}) -> (tensor<?x2xf32>) {
      %0 = stablehlo.constant dense<[-0.211145893, -0.708605706]> : tensor<2xf32>
      %1 = stablehlo.constant dense<[[-0.630731344, 0.54962182], [0.180364341, -0.764542698]]> : tensor<2x2xf32>

    for (int i = 0; i < num_inputs; ++i) {
      if (input_tensors[i] == nullptr) continue;
      all_input_tensors.emplace_back();
      Tensor& input_tensor = all_input_tensors.back();
      status->status = TF_TensorToTensor(input_tensors[i], &input_tensor);
      if (!status->status.ok()) return;
      input_tensors_vector[i] = &input_tensor;
    }
  }

    result_tensor->SetBuffer(std::move(*buffer_or));
  }
  pjrt_buffer->GetReadyFuture().OnReady(std::move(done));
}

void PjRtDeviceContext::CopyTensorInSameDevice(const Tensor* input_tensor,
                                               Device* device,
                                               Tensor* output_tensor,
                                               StatusCallback done) const {