code: def get_feature(internal_layer, layers, filters, batch_norm=False):
      """Get VGG feature body as stacks of convolutions."""
      for i, num in enumerate(layers):
          for j in range(num):
              internal_layer = wrapper.conv2d(
                  data=internal_layer,
                  kernel_size=(3, 3),
                  padding=(1, 1),
                  channels=filters[i],
                  name=f"conv{i + 1}_{j + 1}",
              )
              internal_layer = relay.nn.bias_add(
                  internal_layer, relay.var(f"conv{i + 1}_{j + 1}_bias")
              )
              if batch_norm:
                  internal_layer = wrapper.batch_norm_infer(
                      data=internal_layer, name=f"bn{i + 1}_{j + 1}"
                  )
              internal_layer = relay.nn.relu(data=internal_layer)
          internal_layer = relay.nn.max_pool2d(
              data=internal_layer, pool_size=(2, 2), strides=(2, 2)
          )
      return internal_layer
  
imports: from tvm import relay
  from .init import create_workload
  from .None import layers
label_desc: Use image node_id 1 for calling a global function with example usage: get_feature(internal_layer, layers, filters, batch_norm) and returns: internal_layer