code: def get_net(batch_size, image_shape, num_classes, dtype):
      """Get symbol of SqueezeNet
  
      Parameters
      ----------
      batch_size : int
          The batch size used in the model
  
      image_shape : tuple
          The input image shape
  
      num_classes: int
          The number of classification results
  
      dtype : str
          The data type
  
      """
      data_shape = (batch_size,) + image_shape
      net = relay.var("data", shape=data_shape, dtype=dtype)
      net = relay.nn.conv2d(
          net,
          relay.var("conv1_weight"),
          channels=64,
          kernel_size=(3, 3),
          strides=(2, 2),
          padding=(0, 0),
      )
      net = relay.nn.bias_add(net, relay.var("conv1_bias"))
      net = relay.nn.relu(net)
      net = relay.nn.max_pool2d(net, pool_size=(3, 3), strides=(2, 2))
      net = _make_fire(net, 16, 64, 64, "fire2")
      net = _make_fire(net, 16, 64, 64, "fire3")
      net = relay.nn.max_pool2d(net, pool_size=(3, 3), strides=(2, 2))
      net = _make_fire(net, 32, 128, 128, "fire4")
      net = _make_fire(net, 32, 128, 128, "fire5")
      net = relay.nn.max_pool2d(net, pool_size=(3, 3), strides=(2, 2))
      net = _make_fire(net, 48, 192, 192, "fire6")
      net = _make_fire(net, 48, 192, 192, "fire7")
      net = _make_fire(net, 64, 256, 256, "fire8")
      net = _make_fire(net, 64, 256, 256, "fire9")
      net = relay.nn.dropout(net, rate=0.5)
      net = relay.nn.conv2d(
          net,
          relay.var("conv10_weight"),
          channels=num_classes,
          kernel_size=(1, 1),
      )
      net = relay.nn.bias_add(net, relay.var("conv10_bias"))
      net = relay.nn.relu(net)
      net = relay.nn.global_avg_pool2d(net)
      net = relay.nn.softmax(net, axis=1)
      args = relay.analysis.free_vars(net)
      return relay.Function(args, net)
  
imports: from tvm import relay
  from tvm.relay.testing import create_workload
label_desc: Use image node_id 3 for calling a global function with example usage: get_net(batch_size, image_shape, num_classes, dtype) and returns: relay