code: def test_flatten_unflatten():
      """Tests the flatten and unflatten methods."""
  
      def block(weights, wires):
          qml.CNOT(wires=[wires[0], wires[1]])
          qml.RY(weights[0], wires=wires[0])
          qml.RY(weights[1], wires=wires[1])
  
      n_wires = 4
      n_block_wires = 2
      n_params_block = 2
      n_blocks = qml.MERA.get_n_blocks(range(n_wires), n_block_wires)
  
      template_weights = [[0.1, -0.3]] * n_blocks
  
      wires = qml.wires.Wires((0, 1, 2, 3))
  
      op = qml.MERA(
          wires, n_block_wires, block, n_params_block, template_weights
      )
  
      data, metadata = op._flatten()
      assert len(data) == 1
      assert qml.math.allclose(data[0], template_weights)
  
      assert metadata[0] == wires
      assert dict(metadata[1]) == op.hyperparameters
  
      # make sure metadata hashable
      assert hash(metadata)
  
      new_op = qml.MERA._unflatten(*op._flatten())
      assert qml.equal(new_op, op)
      assert new_op._name == "MERA"  # make sure acutally initialized
      assert new_op is not op
  
imports: import pytest
  import numpy
  import pennylane
  from pennylane.templates.tensornetworks.mera import MERA, compute_indices
label_desc: Use image node_id 7 for calling a global function with example usage: test_flatten_unflatten() without return types