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 gradient can be calculated with respect to all of the trainable variables in
  the model.
docstr_tok: ["Test","if","a","gradient","can","be","calculated","with","respect","to","all","of","the","trainable","variables","in","the","model","."]
code: def test_model_gradients_dm(
      self, model_dm, output_dim, n_qubits
  ):  # pylint: disable=no-self-use,redefined-outer-name
      """Test if a gradient can be calculated with respect to all of the trainable variables in
      the model."""
      x = tf.zeros((2, n_qubits))
      y = tf.zeros((2, output_dim[0] * output_dim[1]))
  
      with tf.GradientTape() as tape:
          out = model_dm(x)
          loss = tf.keras.losses.mean_squared_error(out, y)
  
      gradients = tape.gradient(loss, model_dm.trainable_variables)
      assert all(
          g.dtype == tf.keras.backend.floatx() for g in gradients
      )
  
imports: from collections import defaultdict
  import numpy
  import pytest
  import pennylane
label_desc: Use image node_id 2 for calling the TestKerasLayerIntegrationDM obj's underlying member method code with example usage: obj.test_model_gradients_dm(model_dm, output_dim, n_qubits) without return types