class_docstr: Unit tests for the pennylane.qnn.torch.TorchLayer class.
class_docstr_tok: ["Unit","tests","for","the","pennylane.qnn.torch.TorchLayer","class","."]
docstr: Test if weights are initialized according to the value specified in the
  init_method argument.
docstr_tok: ["Test","if","weights","are","initialized","according","to","the","value","specified","in","the","init_method","argument","."]
code: def test_fixed_init(
      self, get_circuit, n_qubits
  ):  # pylint: disable=no-self-use
      """Test if weights are initialized according to the value specified in the
      init_method argument."""
      c, w = get_circuit
  
      init_method = {
          "w1": torch.normal(mean=0, std=1, size=(3, n_qubits, 3)),
          "w2": torch.normal(mean=0, std=1, size=(1,)),
          "w3": torch.normal(mean=0, std=1, size=[]),
          "w4": torch.normal(mean=0, std=1, size=(3,)),
          "w5": torch.normal(mean=0, std=1, size=(2, n_qubits, 3)),
          "w6": torch.normal(mean=0, std=1, size=(3,)),
          "w7": torch.normal(mean=0, std=1, size=[]),
      }
  
      layer = TorchLayer(
          qnode=c, weight_shapes=w, init_method=init_method
      )
      for name, weight in layer.qnode_weights.items():
          assert weight.data.tolist() == init_method[name].tolist()
  
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 the TestTorchLayer obj's underlying member method code with example usage: obj.test_fixed_init(get_circuit, n_qubits) without return types