code: def _convert_merge(
      inexpr, keras_layer, _, input_shape=None, data_layout=None
  ):  # pylint: disable=unused-argument
      merge_type = type(keras_layer).__name__
      ret = inexpr[0]
      if merge_type == "Dot":
          axes = keras_layer.axes
          if isinstance(keras_layer.axes, int):
              axes = [keras_layer.axes, keras_layer.axes]
          if isinstance(axes, list):
              if len(axes) != 2:
                  raise tvm.error.OpAttributeUnImplemented(
                      f"Dot with axes {keras_layer.axes} is not supported."
                  )
              for i, axis in enumerate(axes):
                  if axis not in [1, 2]:
                      raise tvm.error.OpAttributeUnImplemented(
                          f"Dot with axes {keras_layer.axes} is not supported."
                      )
                  if axes[i] == 2:
                      inexpr[i] = _op.transpose(
                          inexpr[i], axes=[0, 2, 1]
                      )
          else:
              raise tvm.error.OpAttributeUnImplemented(
                  f"Dot with axes {keras_layer.axes} is not supported."
              )
          ret_dot = _op.nn.batch_matmul(inexpr[0], inexpr[1])
          ret = _op.transpose(ret_dot, axes=[0, 2, 1])
      elif merge_type == "Subtract":
          assert len(inexpr) == 2, "Subtract merge takes 2 inputs."
          ret = _op.subtract(ret, inexpr[1])
      elif merge_type in ["Add", "Multiply", "Minimum", "Maximum"]:
          op_map = {
              "Add": _op.add,
              "Multiply": _op.multiply,
              "Minimum": _op.minimum,
              "Maximum": _op.maximum,
          }
          for i in range(1, len(inexpr)):
              ret = op_map[merge_type](ret, inexpr[i])
      elif merge_type == "Average":
          for i in range(1, len(inexpr)):
              ret = _op.add(ret, inexpr[i])
          ret = ret / _expr.const(len(inexpr), dtype="float32")
      else:
          raise tvm.error.OpNotImplemented(
              f"Operator {merge_type} is not supported in frontend Keras."
          )
      return ret
  
imports: import dis
  import sys
  import numpy
  import tvm
  from tvm.ir import IRModule, TensorType, TupleType
  from ..None import analysis
  from ..None import expr
  from ..None import function
  from ..None import op
  from ...None import nd
  from .common import ExprTable, new_var
label_desc: Use image node_id 8 for calling a global function with example usage: _convert_merge(inexpr, keras_layer, _, input_shape, data_layout) and returns: ret