def update(self, doc, process=None):
        if process is None:
            pk = 'id'
            id = doc.id
            doc.synced = False
            set = doc
        elif process == UPDATE_PROCESS:
            if self.db.docs.find({'sid': doc.sid}).count() == 0:
                return self.insert(doc, synced=True)
            pk = 'sid'
            id = doc.sid
            doc.synced = not doc.merged() # must be commited if was merged
            doc.merged(False)
            set = {'tid': doc.tid, 'data': doc.data, 'user': doc.user,
                   'public': doc.public, 'synced': doc.synced}
        elif process == COMMIT_PROCESS:
            pk = 'id'
            id = doc.id
            doc.synced = True
            set = {'sid': doc.sid, 'tid': doc.tid, 'synced': doc.synced}
        else:
            raise DatabaseException('Incorrect update process')

        try:
            rs = self.db.docs.update({pk: id}, {'$set': set}, safe=True)
            assert rs['n'] == 1
        except Exception as e:
            raise DatabaseException(e)
        return doc
def conv2d_fixed_padding(inputs, filters, kernel_size, strides, data_format,
                         name):
  """Strided 2-D convolution with explicit padding."""
  # The padding is consistent and is based only on `kernel_size`, not on the
  # dimensions of `inputs` (as opposed to using `tf.layers.conv2d` alone).
  if strides > 1:
    inputs = fixed_padding(inputs, kernel_size, data_format)

  return tf.compat.v1.layers.conv2d(
      inputs=inputs,
      filters=filters,
      kernel_size=kernel_size,
      strides=strides,
      padding=('SAME' if strides == 1 else 'VALID'),
      use_bias=False,
      reuse=tf.AUTO_REUSE,
      kernel_initializer=tf.compat.v1.variance_scaling_initializer(),
      data_format=data_format,
      name=name)