code: def conv_layer(
      self,
      idx,
      inputs,
      filters,
      size,
      stride,
      weight_name,
      biases_name,
      mode="init_model",
  ):
      channels = inputs.get_shape()[3]
      if mode == "init_model":
          # weight = tf.Variable(tf.truncated_normal([size,size,int(channels),filters], stddev=0.1))
          weight = tf.get_variable(
              name=weight_name[:-2],
              shape=[size, size, int(channels), filters],
              dtype=tf.float32,
          )
          # biases = tf.Variable(tf.constant(0.1, shape=[filters]))
          biases = tf.get_variable(
              name=biases_name[:-2], shape=[filters], dtype=tf.float32
          )
      if mode == "reuse_model":
          weight = tf.get_variable(name=weight_name[:-2])
          biases = tf.get_variable(name=biases_name[:-2])
  
      pad_size = size // 2
      pad_mat = np.array(
          [[0, 0], [pad_size, pad_size], [pad_size, pad_size], [0, 0]]
      )
      inputs_pad = tf.pad(inputs, pad_mat)
  
      conv = tf.nn.conv2d(
          inputs_pad,
          weight,
          strides=[1, stride, stride, 1],
          padding="VALID",
          name=str(idx) + "_conv",
      )
      conv_biased = tf.add(conv, biases, name=str(idx) + "_conv_biased")
      if self.disp_console and mode == "init_model":
          print(
              "Layer  %d : Type = Conv, Size = %d * %d, Stride = %d, Filters = %d, Input channels = %d"
              % (idx, size, size, stride, filters, int(channels))
          )
  
      return tf.maximum(
          self.alpha * conv_biased,
          conv_biased,
          name=str(idx) + "_leaky_relu",
      )
  
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 10 for calling the YOLO_tiny_model_updated obj's underlying member method code with example usage: obj.conv_layer(idx, inputs, filters, size, stride, weight_name, biases_name, mode) and returns: tf