code: def _convert_activation(
      inexpr, keras_layer, etab, data_layout, input_shape=None
  ):  # pylint: disable=unused-argument
      if isinstance(keras_layer, str):
          act_type = keras_layer
      else:
          if sys.version_info.major < 3:
              act_type = keras_layer.activation.func_name
          else:
              act_type = keras_layer.activation.__name__
      if act_type == "linear":
          if isinstance(keras_layer, str):
              return inexpr
          alpha = (
              keras_layer.alpha
              if hasattr(keras_layer, "alpha")
              else 1.0
          )
          beta = (
              keras_layer.beta if hasattr(keras_layer, "beta") else 0.0
          )
          alpha = _expr.const(alpha, dtype="float32")
          beta = _expr.const(beta, dtype="float32")
          return _op.add(_op.multiply(inexpr, alpha), beta)
      if act_type == "softmax":
          axis = 1 if data_layout == "NCHW" else -1
          return _op.nn.softmax(inexpr, axis)
      if act_type == "sigmoid":
          return _op.sigmoid(inexpr)
      if act_type == "tanh":
          return _op.tanh(inexpr)
      if act_type == "relu":
          return _op.nn.relu(inexpr)
      if act_type == "softplus":
          return _op.log(
              _op.add(
                  _op.exp(inexpr), _expr.const(1.0, dtype="float32")
              )
          )
      if act_type == "elu":
          alpha = (
              keras_layer.alpha
              if hasattr(keras_layer, "alpha")
              else 1.0
          )
          alpha = _expr.const(alpha, dtype="float32")
          return _get_elu(inexpr, alpha)
      if act_type == "selu":
          # Alpha, Gamma values obtained from https://arxiv.org/abs/1706.02515
          alpha = (
              keras_layer.alpha
              if hasattr(keras_layer, "alpha")
              else 1.6732632423543772848170429916717
          )
          gamma = (
              keras_layer.gamma
              if hasattr(keras_layer, "gamma")
              else 1.0507009873554804934193349852946
          )
          alpha = _expr.const(alpha, dtype="float32")
          gamma = _expr.const(gamma, dtype="float32")
          return gamma * _get_elu(inexpr, alpha)
      if act_type == "relu6":
          return _op.clip(inexpr, a_min=0.0, a_max=6.0)
      if act_type == "softsign":
          return inexpr / (
              _expr.const(1.0, dtype="float32") + _op.abs(inexpr)
          )
      if act_type == "hard_sigmoid":
          x = (
              _expr.const(0.2, dtype="float32") * inexpr
          ) + _expr.const(0.5, dtype="float32")
          return _op.clip(x, a_min=0.0, a_max=1.0)
      if act_type == "swish":
          return inexpr * _op.sigmoid(inexpr)
  
      raise tvm.error.OpNotImplemented(
          f"Operator {act_type} is not supported in frontend Keras."
      )
  
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 6 for calling a global function with example usage: _convert_activation(inexpr, keras_layer, etab, data_layout, input_shape) and returns: _op, _op, _op, _op, _op, _op, _get_elu, unknown, _op, unknown, _op, unknown, inexpr