code: def fc_layer(
      self,
      idx,
      inputs,
      hiddens,
      weight_name,
      biases_name,
      flat=False,
      linear=False,
      mode="init_model",
  ):
      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
  
      if mode == "init_model":
          # weight = tf.Variable(tf.truncated_normal([dim,hiddens], stddev=0.1))
          weight = tf.get_variable(
              name=weight_name[:-2],
              shape=[dim, hiddens],
              dtype=tf.float32,
          )
          # biases = tf.Variable(tf.constant(0.1, shape=[hiddens]))
          biases = tf.get_variable(
              name=biases_name[:-2], shape=[hiddens], dtype=tf.float32
          )
      if mode == "reuse_model":
          weight = tf.get_variable(name=weight_name[:-2])
          biases = tf.get_variable(name=biases_name[:-2])
  
      if self.disp_console and mode == "init_model":
          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")
  
imports: from __future__ import division
  from __future__ import print_function
  from builtins import str
  from builtins import range
  from past.utils import old_div
  from builtins import object
  import tensorflow
  import cv2
  import numpy
  import os
  import sys
  import argparse
  import pdb
  from tqdm import tqdm
label_desc: Use image node_id 12 for calling the YOLO_tiny_model_updated obj's underlying member method code with example usage: obj.fc_layer(idx, inputs, hiddens, weight_name, biases_name, flat, linear, mode) and returns: tf, tf