class_docstr: Integration tests for the pennylane.qnn.keras.KerasLayer class.
class_docstr_tok: ["Integration","tests","for","the","pennylane.qnn.keras.KerasLayer","class","."]
docstr: Test if a model can train using the KerasLayer. 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",".","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(
      self, model, batch_size, n_qubits, output_dim
  ):  # pylint: disable=no-self-use,redefined-outer-name
      """Test if a model can train using the KerasLayer. 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))
  
      model.compile(optimizer="sgd", loss="mse")
  
      model.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 TestKerasLayerIntegration obj's underlying member method code with example usage: obj.test_train_model(model, batch_size, n_qubits, output_dim) without return types