class_docstr: Branchformer module definition.
  
  Reference: https://arxiv.org/pdf/2207.02971.pdf
  
  Args:
      block_size: Input/output size.
      linear_size: Linear layers' hidden size.
      self_att: Self-attention module instance.
      conv_mod: Convolution module instance.
      norm_class: Normalization class.
      norm_args: Normalization module arguments.
      dropout_rate: Dropout rate.
docstr: Initialize/Reset self-attention and convolution modules cache for streaming.
  
  Args:
      left_context: Number of previous frames the attention module can see
                    in current chunk.
      device: Device to use for cache tensor.
docstr_tok: ["Initialize\/Reset","self-attention","and","convolution","modules","cache","for","streaming",".","Args",":","left_context",":","Number","of","previous","frames","the","attention","module","can","see","in","current","chunk",".","device",":","Device","to","use","for","cache","tensor","."]
code: def reset_streaming_cache(
      self, left_context: int, device: torch.device
  ) -> None:
      """Initialize/Reset self-attention and convolution modules cache for streaming.
  
      Args:
          left_context: Number of previous frames the attention module can see
                        in current chunk.
          device: Device to use for cache tensor.
  
      """
      self.cache = [
          torch.zeros(
              (1, left_context, self.block_size),
              device=device,
          ),
          torch.zeros(
              (
                  1,
                  self.linear_size // 2,
                  self.conv_mod.kernel_size - 1,
              ),
              device=device,
          ),
      ]
  
imports: from typing import Dict, Optional, Tuple
  import torch
label_desc: Use image node_id 2 for calling the Branchformer obj's underlying member method code with example usage: obj.reset_streaming_cache(left_context, device) without return types