docstr: BlockRNN Module forward.
  
  Parameters
  ----------
  x_in
      Tuple of Tensors containing the features of the input sequence. The tuple has elements
      (past target, historic future covariates, future covariates, static covariates).
      The shape of the past target is `(batch_size, input_length, input_size)`.
  
  Returns
  -------
  torch.Tensor
      The BlockRNN output Tensor with shape `(batch_size, output_chunk_length, target_size, nr_params)`.
      It contains the prediction at the last time step of the sequence.
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code: def forward(self, x_in: Tuple) -> torch.Tensor:
      """BlockRNN Module forward.
  
      Parameters
      ----------
      x_in
          Tuple of Tensors containing the features of the input sequence. The tuple has elements
          (past target, historic future covariates, future covariates, static covariates).
          The shape of the past target is `(batch_size, input_length, input_size)`.
  
      Returns
      -------
      torch.Tensor
          The BlockRNN output Tensor with shape `(batch_size, output_chunk_length, target_size, nr_params)`.
          It contains the prediction at the last time step of the sequence.
      """
      pass
  
imports: import inspect
  from abc import ABC, abstractmethod
  from typing import List, Optional, Tuple, Type, Union
  import torch
  import torch.nn
  from darts.logging import get_logger, raise_log
  from darts.models.forecasting.pl_forecasting_module import PLPastCovariatesModule, io_processor
  from darts.models.forecasting.torch_forecasting_model import PastCovariatesTorchModel
label_desc: Use image node_id 2 for calling the CustomBlockRNNModule obj's underlying member method code with example usage: obj.forward(x_in) without return types