class_docstr: Integration tests for the pennylane.qnn.keras.KerasLayer class for
  density_matrix() returning circuits.
class_docstr_tok: ["Integration","tests","for","the","pennylane.qnn.keras.KerasLayer","class","for","density_matrix","(",")","returning","circuits","."]
docstr: Test if a model can train using the KerasLayer when QNode returns a density_matrix().
  The model is composed of two KerasLayers sandwiched between Dense neural network layers,
  and the dataset is simply input and output vectors of zeros.
docstr_tok: ["Test","if","a","model","can","train","using","the","KerasLayer","when","QNode","returns","a","density_matrix","(",")",".","The","model","is","composed","of","two","KerasLayers","sandwiched","between","Dense","neural","network","layers",",","and","the","dataset","is","simply","input","and","output","vectors","of","zeros","."]
code: def test_train_model_dm(
      self, model_dm, batch_size, n_qubits, output_dim
  ):  # pylint: disable=no-self-use,redefined-outer-name
      """Test if a model can train using the KerasLayer when QNode returns a density_matrix().
      The model is composed of two KerasLayers sandwiched between Dense neural network layers,
      and the dataset is simply input and output vectors of zeros."""
  
      x = np.zeros((batch_size, n_qubits))
      y = np.zeros((batch_size, output_dim[0] * output_dim[1]))
  
      model_dm.compile(optimizer="sgd", loss="mse")
  
      model_dm.fit(x, y, batch_size=batch_size, verbose=0)
  
imports: from collections import defaultdict
  import numpy
  import pytest
  import pennylane
label_desc: Use image node_id 1 for calling the TestKerasLayerIntegrationDM obj's underlying member method code with example usage: obj.test_train_model_dm(model_dm, batch_size, n_qubits, output_dim) without return types