code: def multi_head_attention(
      self: ivy.Array,
      /,
      *,
      key: Optional[Union[ivy.Array, ivy.NativeArray]] = None,
      value: Optional[Union[ivy.Array, ivy.NativeArray]] = None,
      num_heads: int = 8,
      scale: Optional[float] = None,
      attention_mask: Optional[
          Union[ivy.Array, ivy.NativeArray]
      ] = None,
      in_proj_weights: Optional[
          Union[ivy.Array, ivy.NativeArray]
      ] = None,
      q_proj_weights: Optional[
          Union[ivy.Array, ivy.NativeArray]
      ] = None,
      k_proj_weights: Optional[
          Union[ivy.Array, ivy.NativeArray]
      ] = None,
      v_proj_weights: Optional[
          Union[ivy.Array, ivy.NativeArray]
      ] = None,
      out_proj_weights: Optional[
          Union[ivy.Array, ivy.NativeArray]
      ] = None,
      in_proj_bias: Optional[Union[ivy.Array, ivy.NativeArray]] = None,
      out_proj_bias: Optional[Union[ivy.Array, ivy.NativeArray]] = None,
      is_causal: bool = False,
      key_padding_mask: Optional[
          Union[ivy.Array, ivy.NativeArray]
      ] = None,
      bias_k: Optional[Union[ivy.Array, ivy.NativeArray]] = None,
      bias_v: Optional[Union[ivy.Array, ivy.NativeArray]] = None,
      static_k: Optional[Union[ivy.Array, ivy.NativeArray]] = None,
      static_v: Optional[Union[ivy.Array, ivy.NativeArray]] = None,
      add_zero_attn: bool = False,
      return_attention_weights: bool = False,
      average_attention_weights: bool = True,
      dropout: float = 0.0,
      training: bool = False,
      out: Optional[ivy.Array] = None,
  ) -> ivy.Array:
      return ivy.multi_head_attention(
          self._data,
          key=key,
          value=value,
          num_heads=num_heads,
          scale=scale,
          attention_mask=attention_mask,
          in_proj_weights=in_proj_weights,
          q_proj_weights=q_proj_weights,
          k_proj_weights=k_proj_weights,
          v_proj_weights=v_proj_weights,
          out_proj_weights=out_proj_weights,
          in_proj_bias=in_proj_bias,
          out_proj_bias=out_proj_bias,
          is_causal=is_causal,
          key_padding_mask=key_padding_mask,
          bias_k=bias_k,
          bias_v=bias_v,
          static_k=static_k,
          static_v=static_v,
          add_zero_attn=add_zero_attn,
          return_attention_weights=return_attention_weights,
          average_attention_weights=average_attention_weights,
          dropout=dropout,
          training=training,
          out=out,
      )
  
file_path: ivy/ivy/data_classes/array/layers.py
imports: import abc
  from typing import Optional, Tuple, Union, List, Sequence
  import ivy
label_desc: Use image node_id 7 for calling the _ArrayWithLayers obj's underlying member method code with example usage: obj.multi_head_attention() and returns: ivy