code: def conv2d(
      x,
      name,
      filter_size,
      in_channels,
      out_channels,
      strides,
      bias=True,
  ):
      """2D convolution."""
      with tf.variable_scope(name):
          kernel = tf.get_variable(
              name="DW",
              shape=[
                  filter_size[0],
                  filter_size[1],
                  in_channels,
                  out_channels,
              ],
              dtype=tf.float32,
              initializer=tf.initializers.glorot_uniform(),
          )
          if bias:
              b = tf.get_variable(
                  name="bias",
                  shape=[out_channels],
                  dtype=tf.float32,
                  initializer=tf.constant_initializer(0.0),
              )
          out = tf.nn.conv2d(
              x, kernel, [1, strides[0], strides[1], 1], padding="SAME"
          )
          if bias:
              out = tf.nn.bias_add(out, b)
          return out
  
file_path: delta/delta/layers/common_layers.py
imports: import delta.compat
  from absl import logging
  from delta.data.feat import speech_ops
label_desc: Use image node_id 3 for calling a global function with example usage: conv2d(x, name, filter_size, in_channels, out_channels, strides, bias) and returns: out