code: def test_batch_input_multi_measure(tol):
      """Test input batching in torch for multiple measurements"""
      dev = qml.device("default.qubit.torch", wires=4)
  
      @qml.qnode(dev, diff_method="parameter-shift")
      def circuit(x, weights):
          qml.AngleEmbedding(x, wires=range(4), rotation="Y")
          qml.RY(weights[0], wires=0)
          qml.RY(weights[1], wires=1)
          return [qml.expval(qml.PauliZ(1)), qml.probs(wires=range(2))]
  
      TorchLayer.set_input_argument("x")
      layer = TorchLayer(circuit, weight_shapes={"weights": (2,)})
      x = torch.Tensor(np.random.uniform(0, 1, (10, 4)))
      res = layer(x)
  
      assert res.shape == (10, 5)
      assert dev.num_executions == 1
  
      for x_, r in zip(x, res):
          exp = torch.hstack(
              circuit(x_, layer.qnode_weights["weights"])
          )
          assert qml.math.allclose(r, exp, atol=tol)
  
      # reset back to the old name
      TorchLayer.set_input_argument("inputs")
  
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 6 for calling a global function with example usage: test_batch_input_multi_measure(tol) and returns: list