code: def _mx_masked_softmax(inputs, attrs):
      assert len(inputs) == 1 or len(inputs) == 2
      axis = attrs.get_int("axis")
      temperature = attrs.get_float("temperature")
      if len(inputs) == 1:
          result = _op.nn.softmax(
              inputs[0] / _expr.const(temperature), axis=axis
          )
      else:
          neg = -1e18
          att_score, mask = inputs
          att_score_dtype = _infer_type(att_score).checked_type.dtype
          if att_score_dtype == "float16":
              neg = -1e4
          temp = _op.where(mask, att_score, _expr.const(neg))
          result = _op.multiply(
              _op.nn.softmax(
                  temp / _expr.const(temperature), axis=axis
              ),
              mask.astype("float32"),
          )
      return result
  
imports: import tvm.relay.op
  import tvm.relay.expr
  from typing import Dict
  from tvm.relay.frontend.mxnet import _convert_map
  from tvm.relay.frontend.common import infer_type
label_desc: Use image node_id 3 for calling a global function with example usage: _mx_masked_softmax(inputs, attrs) and returns: result