class_docstr: Multi-head attention layer.
  
  Args:
      dim (`int`):
          number of features
      num_heads (`int=8`):
          number of heads
      qkv_bias (`bool=False`):
          if add bias to qkv layer
      qk_scale (`float=None`):
          number to scale qk
      attn_drop_ratio (`float=0.`):
          drop rate of attention layer
      proj_drop_ratio (`float=0.`):
          drop rate of projection layer
      with_qkv (`bool=True`):
          if use qkv layer
  
  Example:
      >>> import torch
      >>> from towhee.models.layers.attention import MultiHeadAttention
      >>>
      >>> test_shape = (1, 196+1, 768)  # shape of output from patch_embed
      >>> input_x = torch.rand(test_shape)
      >>> model = MultiHeadAttention(dim=test_shape[2])
      >>> out = model.forward(input_x)
      >>> print(out.shape)
      torch.Size([1, 197, 768])
code: def get_v(self):
      return self.v
  
imports: import os
  import math
  import numpy
  import torch
  from torch import nn
  from towhee.models.layers.layers_with_relprop import Einsum, Linear, Dropout, Softmax
label_desc: Use image node_id 6 for calling the MultiHeadAttention obj's underlying member method code with example usage: obj.get_v() and returns: self