code: def test_specs():
      """Test that the qml.specs transform works for TorchLayer"""
  
      dev = qml.device("default.qubit", wires=3)
      weight_shapes = {"w1": 1, "w2": (3, 2, 3)}
  
      @qml.qnode(dev, interface="torch")
      def circuit(inputs, w1, w2):
          qml.templates.AngleEmbedding(inputs, wires=[0, 1])
          qml.RX(w1, wires=0)
          qml.templates.StronglyEntanglingLayers(w2, wires=[0, 1])
          return qml.expval(qml.PauliZ(0)), qml.expval(qml.PauliZ(1))
  
      qlayer = TorchLayer(circuit, weight_shapes)
  
      batch_size = 5
      x = torch.Tensor(np.random.uniform(0, 1, (batch_size, 2)))
  
      info = qml.specs(qlayer)(x)
  
      gate_sizes = defaultdict(int, {1: 1, 2: 2})
      gate_types = defaultdict(
          int,
          {"AngleEmbedding": 1, "RX": 1, "StronglyEntanglingLayers": 1},
      )
      expected_resources = qml.resource.Resources(
          num_wires=2,
          num_gates=3,
          gate_types=gate_types,
          gate_sizes=gate_sizes,
          depth=3,
      )
      assert info["resources"] == expected_resources
  
      assert info["num_observables"] == 2
      assert info["num_diagonalizing_gates"] == 0
      assert info["num_device_wires"] == 2
      assert info["num_trainable_params"] == 0
      assert info["interface"] == "torch"
      assert info["device_name"] == "default.qubit"
  
imports: from unittest import mock
  import math
  from collections import defaultdict
  import numpy
  import pytest
  import pennylane
  from pennylane.qnn.torch import TorchLayer
label_desc: Use image node_id 9 for calling a global function with example usage: test_specs() and returns: qml,  qml