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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def image_feature_engineering(features, feature_tensors_dict):
 """Add a hidden layer on image features. Args: features: features dict feature_tensors_dict: dict of feature-name: tensor """

  engineered_features = {}
  for name, feature_tensor in six.iteritems(feature_tensors_dict):
    if name in features and features[name]['transform'] == IMAGE_TRANSFORM:
      with tf.name_scope(name, 'Wx_plus_b'):
        hidden = tf.contrib.layers.fully_connected(
            feature_tensor,
            IMAGE_HIDDEN_TENSOR_SIZE)
        engineered_features[name] = hidden
    else:
      engineered_features[name] = feature_tensor
  return engineered_features