code: def transformer_encode(
      src,
      embed_dim,
      num_heads,
      dim_feedforward=2048,
      dropout=0.1,
      activation=None,
      src_additive_mask=None,
      src_key_padding_mask=None,
      rng=None,
      add_attn_bias=False,
      fix_parameters=False,
  ):
      r"""Transformer Encoder.
  
      Args:
          src (~nnabla.Variable): Input sequnce to the encoder layer with shape :math:`(L_S, 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.
          src_additive_mask (~nnabla.Variable, optional): Additive mask for the source sequence with shape :math:`(L_S, L_S)`
          src_key_padding_mask (~nnabla.Variable, optional): Padding mask for the source 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, optional): Random generator for Initializer. Defalut is None.
          add_attn_bias (bool, optional): Specify whether to add attention bias parameters for key and value. Default is False.
          fix_parameters (bool, optional): 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_S, B, E)`
      """
      if activation is None:
          activation = F.relu
      src_self_attn = multi_head_attention(
          src,
          src,
          src,
          num_heads=num_heads,
          add_attn_bias=add_attn_bias,
          additive_mask=src_additive_mask,
          key_padding_mask=src_key_padding_mask,
          name="src_self_attn",
      )[0]
      if dropout > 0:
          src_self_attn = F.dropout(src_self_attn, dropout)
      src_self_attn = src + src_self_attn
      src = layer_normalization(
          src_self_attn, batch_axis=(0, 1), name="enc_layer_norm1"
      )
      src_affine = activation(
          affine(src, dim_feedforward, base_axis=2, name="enc_affine1")
      )
      if dropout > 0:
          src_affine = F.dropout(src_affine, dropout)
      src_affine = affine(
          src_affine, embed_dim, base_axis=2, name="enc_affine2"
      )
      if dropout > 0:
          src_affine = F.dropout(src_affine, dropout)
      src_affine = src + src_affine
      src = layer_normalization(
          src_affine, batch_axis=(0, 1), name="enc_layer_norm2"
      )
      return src
  
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 47 for calling a global function with example usage: transformer_encode(src, embed_dim, num_heads, dim_feedforward, dropout, activation, src_additive_mask, src_key_padding_mask, rng, add_attn_bias, fix_parameters) and returns: src