code: def get_net(
      batch_size,
      num_actions=18,
      image_shape=(4, 84, 84),
      dtype="float32",
      layout="NCHW",
  ):
      """get symbol of nature dqn"""
      data_shape = (batch_size,) + image_shape
      data = relay.var("data", shape=data_shape, dtype=dtype)
  
      bias_axis = layout.index("C")
  
      conv1_bias = relay.var("conv1_bias")
      conv1 = layers.conv2d(
          data,
          kernel_size=(8, 8),
          strides=(4, 4),
          padding=(0, 0),
          channels=32,
          name="conv1",
          data_layout=layout,
          kernel_layout=layers.conv_kernel_layout(layout),
      )
      conv1 = relay.nn.bias_add(conv1, conv1_bias, bias_axis)
      relu1 = relay.nn.relu(conv1)
  
      conv2_bias = relay.var("conv2_bias")
      conv2 = layers.conv2d(
          relu1,
          kernel_size=(4, 4),
          strides=(2, 2),
          padding=(0, 0),
          channels=64,
          name="conv2",
          data_layout=layout,
          kernel_layout=layers.conv_kernel_layout(layout),
      )
      conv2 = relay.nn.bias_add(conv2, conv2_bias, bias_axis)
      relu2 = relay.nn.relu(conv2)
  
      conv3_bias = relay.var("conv3_bias")
      conv3 = layers.conv2d(
          relu2,
          kernel_size=(3, 3),
          strides=(1, 1),
          padding=(0, 0),
          channels=64,
          name="conv3",
          data_layout=layout,
          kernel_layout=layers.conv_kernel_layout(layout),
      )
      conv3 = relay.nn.bias_add(conv3, conv3_bias, bias_axis)
      relu3 = relay.nn.relu(conv3)
  
      bf1 = relay.nn.batch_flatten(relu3)
      dense1 = layers.dense_add_bias(bf1, units=512, name="dense1")
      relu4 = relay.nn.relu(dense1)
      dense2 = layers.dense_add_bias(
          relu4, units=num_actions, name="dense2"
      )
  
      args = relay.analysis.free_vars(dense2)
      return relay.Function(args, dense2)
  
imports: from tvm import relay
  from .None import layers
  from .init import create_workload
label_desc: Use image node_id 1 for calling a global function with example usage: get_net(batch_size, num_actions, image_shape, dtype, layout) and returns: relay