class_docstr: MultiBlocks definition.
  
  Args:
      block_list: Individual blocks of the encoder architecture.
      output_size: Architecture output size.
      norm_class: Normalization module class.
      norm_args: Normalization module arguments.
      blockdrop_rate: Probability threshold of dropping out each block.
docstr: Forward each block of the encoder architecture.
  
  Args:
      x: MultiBlocks input sequences. (B, T, D_block_1)
      pos_enc: Positional embedding sequences. (B, 2 * (T - 1), D_att)
      mask: Source mask. (B, T_2)
      left_context: Number of previous frames the attention module can see
                    in current chunk (used by Conformer and Branchformer block).
  
  Returns:
      x: MultiBlocks output sequences. (B, T, D_block_N)
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code: def chunk_forward(
      self,
      x: torch.Tensor,
      pos_enc: torch.Tensor,
      mask: torch.Tensor,
      left_context: int = 0,
  ) -> torch.Tensor:
      """Forward each block of the encoder architecture.
  
      Args:
          x: MultiBlocks input sequences. (B, T, D_block_1)
          pos_enc: Positional embedding sequences. (B, 2 * (T - 1), D_att)
          mask: Source mask. (B, T_2)
          left_context: Number of previous frames the attention module can see
                        in current chunk (used by Conformer and Branchformer block).
  
      Returns:
          x: MultiBlocks output sequences. (B, T, D_block_N)
  
      """
      for block in self.blocks:
          x, pos_enc = block.chunk_forward(
              x,
              pos_enc,
              mask,
              left_context=left_context,
          )
  
      x = self.norm_blocks(x)
  
      return x
  
imports: from typing import Dict, List, Optional
  import torch
label_desc: Use image node_id 4 for calling the MultiBlocks obj's underlying member method code with example usage: obj.chunk_forward(x, pos_enc, mask, left_context) and returns: x