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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def TransformerEncoder(vocab_size, num_classes=10, feature_depth=512, feedforward_depth=2048, num_layers=6, num_heads=8, dropout=0.1, max_len=2048, mode='train'):
 """Transformer encoder. Args: vocab_size: int: vocab size num_classes: how many classes on output feature_depth: int: depth of embedding feedforward_depth: int: depth of feed-forward layer num_layers: int: number of encoder/decoder layers num_heads: int: number of attention heads dropout: float: dropout rate (how much to drop out) max_len: int: maximum symbol length for positional encoding mode: str: 'train' or 'eval' Returns: the Transformer encoder layer. """

  input_embedding = layers.Serial(
      layers.Embedding(feature_depth, vocab_size),
      layers.Dropout(rate=dropout, mode=mode),
      layers.PositionalEncoding(max_len=max_len)
  )
  return layers.Serial(
      layers.Branch(),  # Branch input to create embedding and mask.
      layers.Parallel(input_embedding, layers.PaddingMask()),
      layers.Serial(*[EncoderLayer(feature_depth, feedforward_depth, num_heads,
                                   dropout, mode)
                      for _ in range(num_layers)]),
      layers.FirstBranch(),  # Drop the mask.
      layers.LayerNorm(),
      layers.Mean(axis=1),  # Average on length.
      layers.Dense(num_classes),
      layers.LogSoftmax()
  )