how to throw some of samples within a mini-batch during training

I'm using AdamOptimizer to train a simple DNN network.(I'm using version tf1.4).
And I want to throw away some bad samples within a batch during training. Say I have 4096 samples within a batch, and I want to throw 96 samples away and only use the remaining 4000 samples to calculate loss and do backpropagation.
How can I achieve this?
The code set up is very straightforward like below:

lables = tf.reshape(labels, [batch_size, 1])
logits = tf.reshape(logits, [batch_size, 1])
loss_vector = tf.nn.sigmoid_cross_entropy_with_logits(multi_class_labels=labels,
                                               logits=logits)
loss_scalar = tf.reduce_mean(loss_vector)
opt = tf.train.AdamOptimizer(learning_rate=learning_rate)
train_op = opt.minimize(loss_scalar, global_step=global_step)

one possible solution is to do a mask operation after loss_vector and before reduce_mean. But I'm not sure if it's the right solution and I have some questions about what's going on underhood:

1. in the minimize() operation, since the input parameter is a scalar, how will it know that some of input samples are masked out?
2. in the minimize() operation, how many times of backpropagation will happen?
3. during training, samples are feed in the graph one by one, how can TF know that which should be kept and which should throw away?