code: def forward(self, x, mask):
      out = self._fc(x)
      query, key, value = mx.np.split(out, 3, axis=-1)
      if self.no_split_case:
          key = mx.np.concat((key, key), axis=1)
          value = mx.np.concat((value, value), axis=1)
      query = mx.np.reshape(query, (-2, -2, self._num_heads, -1))
      if self.negative_case:
          query = query * 2
      key = mx.np.reshape(key, (-2, -2, self._num_heads, -1))
      value = mx.np.reshape(value, (-2, -2, self._num_heads, -1))
      scores = mx.npx.batch_dot(
          mx.np.swapaxes(query, 1, 2),
          mx.np.swapaxes(key, 1, 2),
          transpose_b=True,
      )
      mask = mx.np.expand_dims(mask, axis=1).astype(np.bool)
      attn_weights = mx.npx.masked_softmax(
          scores, mask=mask, axis=-1, temperature=self._scale
      )
      attn_weights = mx.npx.dropout(attn_weights, p=0.1)
      context_vec = mx.npx.batch_dot(
          attn_weights, mx.np.swapaxes(value, 1, 2)
      ).transpose((0, 2, 1, 3))
      context_vec = mx.npx.reshape(context_vec, (-2, -2, -1))
      return context_vec
  
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 2 for calling the MultiHeadAttention obj's underlying member method code with example usage: obj.forward(x, mask) and returns: context_vec