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: Initialize/Reset encoder streaming cache.
  
  Args:
      left_context: Number of previous frames the attention module can see
                    in current chunk (used by Conformer and Branchformer block).
      device: Device to use for cache tensor.
docstr_tok: ["Initialize\/Reset","encoder","streaming","cache",".","Args",":","left_context",":","Number","of","previous","frames","the","attention","module","can","see","in","current","chunk","(","used","by","Conformer","and","Branchformer","block",")",".","device",":","Device","to","use","for","cache","tensor","."]
code: def reset_streaming_cache(
      self, left_context: int, device: torch.device
  ) -> None:
      """Initialize/Reset encoder streaming cache.
  
      Args:
          left_context: Number of previous frames the attention module can see
                        in current chunk (used by Conformer and Branchformer block).
          device: Device to use for cache tensor.
  
      """
      for block in self.blocks:
          block.reset_streaming_cache(left_context, device)
  
imports: from typing import Dict, List, Optional
  import torch
label_desc: Use image node_id 2 for calling the MultiBlocks obj's underlying member method code with example usage: obj.reset_streaming_cache(left_context, device) without return types