old_contents: # Licensed to the Apache Software Foundation (ASF) under one
  # or more contributor license agreements.  See the NOTICE file
  # distributed with this work for additional information
  # regarding copyright ownership.  The ASF licenses this file
  # to you under the Apache License, Version 2.0 (the
  # "License"); you may not use this file except in compliance
  # with the License.  You may obtain a copy of the License at
  #
  #   http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing,
  # software distributed under the License is distributed on an
  # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
  # KIND, either express or implied.  See the License for the
  # specific language governing permissions and limitations
  # under the License.
  
  import mxnet as mx
  
  def factorization_machine_model(factor_size, num_features,
                                  lr_mult_config, wd_mult_config, init_config):
      """ builds factorization machine network with proper formulation:
      y = w_0 \sum(x_i w_i) + 0.5(\sum\sum<v_i,v_j>x_ix_j - \sum<v_iv_i>x_i^2)
      """
      x = mx.symbol.Variable("data", stype='csr')
      # factor, linear and bias terms
      v = mx.symbol.Variable("v", shape=(num_features, factor_size), stype='row_sparse',
                             init=init_config['v'], lr_mult=lr_mult_config['v'],
                             wd_mult=wd_mult_config['v'])
      w = mx.symbol.Variable('w', shape=(num_features, 1), stype='row_sparse',
                        init=init_config['w'], lr_mult=lr_mult_config['w'],
                        wd_mult=wd_mult_config['w'])
      w0 = mx.symbol.Varialble('w0', shape=(1,), init=init_config['w0'],
                         lr_mult=lr_mult_config['w0'], wd_mult=wd_mult_config['w0'])
      w1 = mx.symbol.broadcast_add(mx.symbol.dot(x, w), w0)
  
      # squared terms for subtracting self interactions
      v_s = mx.symbol._internal._square_sum(data=v, axis=1, keepdims=True)
      x_s = x.square()
      bd_sum = mx.sym.dot(x_s, v_s)
  
      # interactions
      w2 = mx.symbol.dot(x, v)
      w2_squared = 0.5 * mx.symbol.square(data=w2)
  
      # putting everything together
      w_all = mx.symbol.Concat(w1, w2_squared, dim=1)
      sum1 = w_all.sum(axis=1, keepdims=True)
      sum2 = -0.5 * bd_sum
      model = sum1 + sum2
  
      y = mx.symbol.Variable("softmax_label")
      model = mx.symbol.LogisticRegressionOutput(data=model, label=y)
      return model
  
new_contents: # Licensed to the Apache Software Foundation (ASF) under one
  # or more contributor license agreements.  See the NOTICE file
  # distributed with this work for additional information
  # regarding copyright ownership.  The ASF licenses this file
  # to you under the Apache License, Version 2.0 (the
  # "License"); you may not use this file except in compliance
  # with the License.  You may obtain a copy of the License at
  #
  #   http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing,
  # software distributed under the License is distributed on an
  # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
  # KIND, either express or implied.  See the License for the
  # specific language governing permissions and limitations
  # under the License.
  
  import mxnet as mx
  
  def factorization_machine_model(factor_size, num_features,
                                  lr_mult_config, wd_mult_config, init_config):
      """ builds factorization machine network with proper formulation:
      y = w_0 \sum(x_i w_i) + 0.5(\sum\sum<v_i,v_j>x_ix_j - \sum<v_iv_i>x_i^2)
      """
      x = mx.symbol.Variable("data", stype='csr')
      # factor, linear and bias terms
      v = mx.symbol.Variable("v", shape=(num_features, factor_size), stype='row_sparse',
                             init=init_config['v'], lr_mult=lr_mult_config['v'],
                             wd_mult=wd_mult_config['v'])
      w = mx.symbol.Variable('w', shape=(num_features, 1), stype='row_sparse',
                        init=init_config['w'], lr_mult=lr_mult_config['w'],
                        wd_mult=wd_mult_config['w'])
      w0 = mx.symbol.Variable('w0', shape=(1,), init=init_config['w0'],
                         lr_mult=lr_mult_config['w0'], wd_mult=wd_mult_config['w0'])
      w1 = mx.symbol.broadcast_add(mx.symbol.dot(x, w), w0)
  
      # squared terms for subtracting self interactions
      v_s = mx.symbol._internal._square_sum(data=v, axis=1, keepdims=True)
      x_s = x.square()
      bd_sum = mx.sym.dot(x_s, v_s)
  
      # interactions
      w2 = mx.symbol.dot(x, v)
      w2_squared = 0.5 * mx.symbol.square(data=w2)
  
      # putting everything together
      w_all = mx.symbol.Concat(w1, w2_squared, dim=1)
      sum1 = w_all.sum(axis=1, keepdims=True)
      sum2 = -0.5 * bd_sum
      model = sum1 + sum2
  
      y = mx.symbol.Variable("softmax_label")
      model = mx.symbol.LogisticRegressionOutput(data=model, label=y)
      return model
  
subject: Make API call consistent