code: def __init__(
      self,
      emformer_layers: torch.nn.ModuleList,
      segment_length: int,
      left_context_length: int = 0,
      right_context_length: int = 0,
      max_memory_size: int = 0,
  ):
      super().__init__()
  
      self.use_mem = max_memory_size > 0
      self.memory_op = torch.nn.AvgPool1d(
          kernel_size=segment_length,
          stride=segment_length,
          ceil_mode=True,
      )
      self.emformer_layers = emformer_layers
      self.left_context_length = left_context_length
      self.right_context_length = right_context_length
      self.segment_length = segment_length
      self.max_memory_size = max_memory_size
  
imports: import math
  from typing import List, Optional, Tuple
  import torch
label_desc: Use image node_id 1 to create a new _EmformerImpl object from inherited base classes: torch.nn with example: obj = _EmformerImpl(emformer_layers, segment_length, left_context_length, right_context_length, max_memory_size)