code: def model_dm(get_circuit_dm, n_qubits, output_dim):
      """The Keras NN model."""
      c, w = get_circuit_dm
      layer1 = KerasLayer(c, w, output_dim)
      layer2 = KerasLayer(c, w, output_dim)
  
      m = tf.keras.models.Sequential(
          [
              tf.keras.layers.Dense(n_qubits, input_shape=(n_qubits,)),
              layer1,
              # Adding a lambda layer to take only the real values from density matrix
              tf.keras.layers.Lambda(
                  lambda x: tf.abs(x)
              ),  # pylint: disable=unnecessary-lambda
              tf.keras.layers.Flatten(),
              tf.keras.layers.Dense(n_qubits),
              layer2,
              # Adding a lambda layer to take only the real values from density matrix
              tf.keras.layers.Lambda(
                  lambda x: tf.abs(x)
              ),  # pylint: disable=unnecessary-lambda
              tf.keras.layers.Flatten(),
              tf.keras.layers.Dense(output_dim[0] * output_dim[1]),
          ]
      )
  
      return m
  
imports: from collections import defaultdict
  import numpy
  import pytest
  import pennylane
label_desc: Use image node_id 2 for calling a global function with example usage: model_dm(get_circuit_dm, n_qubits, output_dim) and returns: m