class_docstr: Abstract class for the encoder.
  
  An encoder takes a `target` sequence with corresponding covariates and maps
  it into a static latent and a dynamic latent code with the same length as
  the `target` sequence.
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docstr: Assemble features from target, static features, and the dynamic
  features.
  
  Parameters
  ----------
  F
      A module that can either refer to the Symbol API or the NDArray
      API in MXNet.
  target
      target time series,
      shape (batch_size, sequence_length, 1)
  static_features
      static features,
      shape (batch_size, num_feat_static)
  dynamic_features
      dynamic_features,
      shape (batch_size, sequence_length, num_feat_dynamic)
  
  Returns
  -------
  Tensor
      combined features,
      shape (batch_size, sequence_length,
             num_feat_static + num_feat_dynamic + 1)
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code: def _assemble_inputs(
      self,
      F,
      target: Tensor,
      static_features: Tensor,
      dynamic_features: Tensor,
  ) -> Tensor:
      """
      Assemble features from target, static features, and the dynamic
      features.
  
      Parameters
      ----------
      F
          A module that can either refer to the Symbol API or the NDArray
          API in MXNet.
      target
          target time series,
          shape (batch_size, sequence_length, 1)
      static_features
          static features,
          shape (batch_size, num_feat_static)
      dynamic_features
          dynamic_features,
          shape (batch_size, sequence_length, num_feat_dynamic)
  
      Returns
      -------
      Tensor
          combined features,
          shape (batch_size, sequence_length,
                 num_feat_static + num_feat_dynamic + 1)
      """
  
      helper_ones = F.ones_like(target)  # Ones of (N, T, 1)
      tiled_static_features = F.batch_dot(
          helper_ones, static_features.expand_dims(1)
      )  # (N, T, C)
      inputs = F.concat(
          target, tiled_static_features, dynamic_features, dim=2
      )  # (N, T, C)
      return inputs
  
imports: from typing import List, Tuple
  from mxnet.gluon import nn
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
  from gluonts.mx.block.cnn import CausalConv1D
  from gluonts.mx.block.mlp import MLP
  from gluonts.mx.block.rnn import RNN
label_desc: Use image node_id 2 for calling the Seq2SeqEncoder obj's underlying member method code with example usage: obj._assemble_inputs(F, target, static_features, dynamic_features) and returns: inputs