code: def fc_layer(self, idx, inputs, hiddens, flat=False, linear=False):
      input_shape = inputs.get_shape().as_list()
  
      if flat:
          dim = input_shape[1] * input_shape[2] * input_shape[3]
          inputs_transposed = tf.transpose(inputs, (0, 3, 1, 2))
          inputs_processed = tf.reshape(inputs_transposed, [-1, dim])
      else:
          dim = input_shape[1]
          inputs_processed = inputs
  
      weight = tf.Variable(
          tf.truncated_normal([dim, hiddens], stddev=0.1)
      )
      biases = tf.Variable(tf.constant(0.1, shape=[hiddens]))
  
      if self.disp_console:
          print(
              "    Layer  %d : Type = Full, Hidden = %d, Input dimension = %d, Flat = %d, Activation = %d"
              % (idx, hiddens, int(dim), int(flat), 1 - int(linear))
          )
  
      if linear:
          return tf.add(
              tf.matmul(inputs_processed, weight),
              biases,
              name=str(idx) + "_fc",
          )
  
      ip = tf.add(tf.matmul(inputs_processed, weight), biases)
  
      return tf.maximum(self.alpha * ip, ip, name=str(idx) + "_fc")
  
file_path: AdvBox/applications/StealthTshirt/StealthTshirtDemo.py
imports: import numpy
  import tensorflow
  import cv2
  import time
  import sys
  import os
  import pdb
label_desc: Use image node_id 6 for calling the Faster_YOLO obj's underlying member method code with example usage: obj.fc_layer(idx, inputs, hiddens, flat, linear) and returns: tf, tf