code: def conv(
      self,
      inp,
      k_h,
      k_w,
      c_o,
      s_h,
      s_w,
      name,
      relu=True,
      padding="SAME",
      group=1,
      biased=True,
  ):
      # Verify that the padding is acceptable
      self.validate_padding(padding)
      # Get the number of channels in the input
      c_i = inp.get_shape()[-1]
      # Verify that the grouping parameter is valid
      assert c_i % group == 0
      assert c_o % group == 0
      # Convolution for a given input and kernel
      convolve = lambda i, k: tf.nn.conv2d(
          i, k, [1, s_h, s_w, 1], padding=padding
      )
      with tf.variable_scope(name) as scope:
          kernel = self.make_var(
              "weights", shape=[k_h, k_w, c_i // group, c_o]
          )
          # This is the common-case. Convolve the input without any further complications.
          output = convolve(inp, kernel)
          # Add the biases
          if biased:
              biases = self.make_var("biases", [c_o])
              output = tf.nn.bias_add(output, biases)
          if relu:
              # ReLU non-linearity
              output = tf.nn.relu(output, name=scope.name)
          return output
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import numpy
  import tensorflow
  import cv2
  import os
  import inspect
label_desc: Use image node_id 9 for calling the Network obj's underlying member method code with example usage: obj.conv(inp, k_h, k_w, c_o, s_h, s_w, name, relu, padding, group, biased) and returns: output