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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 toy_model(features, mesh):
 """A toy model implemented by mesh tensorlfow."""

  batch_dim = mtf.Dimension('batch', FLAGS.batch_size)
  io_dim = mtf.Dimension('io', FLAGS.io_size)

  master_dtype = tf.as_dtype(FLAGS.master_dtype)
  slice_dtype = tf.as_dtype(FLAGS.slice_dtype)
  activation_dtype = tf.as_dtype(FLAGS.activation_dtype)

  x = mtf.import_tf_tensor(mesh, features, mtf.Shape([batch_dim, io_dim]))
  x = mtf.cast(x, activation_dtype)
  h = x
  for lnum in xrange(1, FLAGS.num_hidden_layers + 2):
    if lnum + 1 == FLAGS.num_hidden_layers + 2:
      # output layer
      dim = io_dim
    elif lnum % 2 == 0:
      dim = mtf.Dimension('hidden_even', FLAGS.hidden_size)
    else:
      dim = mtf.Dimension('hidden_odd', FLAGS.hidden_size)
    h = mtf.layers.dense(
        h, dim,
        use_bias=False,
        master_dtype=master_dtype,
        slice_dtype=slice_dtype,
        name='layer_%d' % lnum)
  y = h
  g = tf.train.get_global_step()
  if FLAGS.step_with_nan >= 0:
    # Trigger NaN in the forward pass, this is used for testing whether
    # MeshTensorFlow can handle occasional NaN value.
    y += mtf.import_tf_tensor(
        mesh,
        tf.divide(
            0.0,
            tf.cond(tf.equal(g, FLAGS.step_with_nan), lambda: 0., lambda: 1.)),
        mtf.Shape([]))

  loss = mtf.reduce_mean(mtf.square(y - x))
  return y, loss