code: def __init__(
      self,
      units,
      num_heads,
      batch_size=-1,
      seq_length=-1,
      dtype="float32",
      negative_case=False,
      no_split_case=False,
      **kwargs,
  ):
      super(MultiHeadAttention, self).__init__(**kwargs)
      self._units = units
      self._num_heads = num_heads
      self._fc = nn.Dense(
          in_units=self._units,
          units=3 * self._units,
          flatten=False,
          dtype=dtype,
      )
      self._scale = math.sqrt(self._units // self._num_heads)
      self.negative_case = negative_case
      self.no_split_case = no_split_case
      self.batch_size = batch_size
      self.seq_length = seq_length
  
file_path: mxnet/tests/python/dnnl/subgraphs/test_matmul_subgraph.py
imports: import copy
  import mxnet
  import numpy
  import pytest
  from mxnet.contrib import quantization
  from mxnet.gluon import nn
  from mxnet.test_utils import assert_almost_equal, assert_almost_equal_with_err
  from mxnet.util import use_np
  import math
label_desc: Use image node_id 1 to create a new MultiHeadAttention object from inherited base classes: nn with example: obj = MultiHeadAttention(units, num_heads, batch_size, seq_length, dtype, negative_case, no_split_case)