code: def transformer_decode(
      tgt,
      memory,
      embed_dim,
      num_heads,
      dim_feedforward=2048,
      dropout=0.1,
      activation=None,
      tgt_additive_mask=None,
      memory_additive_mask=None,
      tgt_key_padding_mask=None,
      memory_key_padding_mask=None,
      rng=None,
      add_attn_bias=False,
      fix_parameters=False,
  ):
      r"""Transformer Decoder.
  
      Args:
          tgt (~nnabla.Variable): Input sequnce to the decoder layer with shape :math:`(L_T, B, E)`.
          memory (~nnabla.Variable): Output sequnce from the last layer of the encoder with shape :math:`(L_T, B, E)`.
          embed_dim (int): Number of embedding dimension.
          num_heads (int): Number of attention heads.
          dim_feedforward (int, optional): Dimension of the feedforward network model. Default is 2048.
          dropout (float, optional): Dropout ratio. Default is 0.1.
          activation (function, optional): Non-linear activation function to be used. Default is None, which is set as F.relu in the code.
          tgt_additive_mask (~nnabla.Variable, optional): Additive mask for the target sequence with shape :math:`(L_T, L_T)`.
          memory_additive_mask (~nnabla.Variable, optional): Additive mask for the memory sequcne with shape :math:`(L_T, L_S)`.
          tgt_key_padding_mask (~nnabla.Variable, optional): Padding mask for the target sequence with shape :math:`(B, L_T)`. Specified padding elements will be ignored by the attention layer. Values must be either 1 or 0.
          memory_key_padding_mask (~nnabla.Variable, optional): Padding mask for the mask sequence with shape :math:`(B, L_S)`. Specified padding elements will be ignored by the attention layer. Values must be either 1 or 0.
          rng (numpy.random.RandomState): Random generator for Initializer. Default is None.
          add_attn_bias (bool, optional): Specify whether to add attention bias parameters for key and value. Default is False.
          fix_parameters (bool): When set to `True`, the weights and biases will not be updated. Default is False.
  
      Returns:
          ~nnabla.Variable: Output :math:`y` with shape :math:`(L_T, B, E)`
      """
      if activation is None:
          activation = F.relu
      tgt_self_attn = multi_head_attention(
          tgt,
          tgt,
          tgt,
          num_heads=num_heads,
          add_attn_bias=add_attn_bias,
          additive_mask=tgt_additive_mask,
          key_padding_mask=tgt_key_padding_mask,
          name="tgt_self_attn",
      )[0]
      if dropout > 0:
          tgt_self_attn = F.dropout(tgt_self_attn, dropout)
      tgt_self_attn = tgt + tgt_self_attn
      tgt = layer_normalization(
          tgt_self_attn, batch_axis=(0, 1), name="dec_layer_norm1"
      )
      tgt_multi_attn = multi_head_attention(
          tgt,
          memory,
          memory,
          num_heads=num_heads,
          add_attn_bias=add_attn_bias,
          additive_mask=memory_additive_mask,
          key_padding_mask=memory_key_padding_mask,
          name="tgt_memory_attn",
      )[0]
      if dropout > 0:
          tgt_multi_attn = F.dropout(tgt_multi_attn, dropout)
      tgt_multi_attn = tgt + tgt_multi_attn
      tgt = layer_normalization(
          tgt_multi_attn, batch_axis=(0, 1), name="dec_layer_norm2"
      )
      tgt_affine = activation(
          affine(tgt, dim_feedforward, base_axis=2, name="dec_affine1")
      )
      if dropout > 0:
          tgt_affine = F.dropout(tgt_affine, dropout)
      tgt_affine = affine(
          tgt_affine, embed_dim, base_axis=2, name="dec_affine2"
      )
      if dropout > 0:
          tgt_affine = F.dropout(tgt_affine, dropout)
      tgt_affine = tgt + tgt_affine
      tgt = layer_normalization(
          tgt_affine, batch_axis=(0, 1), name="dec_layer_norm3"
      )
  
      return tgt
  
imports: from six import exec_
  import numpy
  import nnabla
  import nnabla.functions
  from nnabla.parameter import get_parameter_or_create, get_parameter
  from nnabla.initializer import calc_uniform_lim_glorot, ConstantInitializer, NormalInitializer, UniformInitializer, WeightNormalizationScaleInitializer
label_desc: Use image node_id 48 for calling a global function with example usage: transformer_decode(tgt, memory, embed_dim, num_heads, dim_feedforward, dropout, activation, tgt_additive_mask, memory_additive_mask, tgt_key_padding_mask, memory_key_padding_mask, rng, add_attn_bias, fix_parameters) and returns: tgt