class_docstr: This is an implementation of Conformer encoder layer.
  
  Arguments
  ----------
  d_model : int
      The expected size of the input embedding.
  d_ffn : int
      Hidden size of self-attention Feed Forward layer.
  nhead : int
      Number of attention heads.
  kernel_size : int, optional
      Kernel size of convolution model.
  kdim : int, optional
      Dimension of the key.
  vdim : int, optional
      Dimension of the value.
  activation: torch.nn.Module
       Activation function used in each Conformer layer.
  bias : bool, optional
      Whether  convolution module.
  dropout : int, optional
      Dropout for the encoder.
  causal: bool, optional
      Whether the convolutions should be causal or not.
  attention_type: str, optional
      type of attention layer, e.g. regulaMHA for regular MultiHeadAttention.
  
  Example
  -------
  >>> import torch
  >>> x = torch.rand((8, 60, 512))
  >>> pos_embs = torch.rand((1, 2*60-1, 512))
  >>> net = ConformerEncoderLayer(d_ffn=512, nhead=8, d_model=512, kernel_size=3)
  >>> output = net(x, pos_embs=pos_embs)
  >>> output[0].shape
  torch.Size([8, 60, 512])
docstr: Creates a blank streaming context for this encoding layer.
  
  Arguments
  ---------
  mha_left_context_size : int
      How many left frames should be saved and used as left context to the
      current chunk when streaming
docstr_tok: ["Creates","a","blank","streaming","context","for","this","encoding","layer",".","Arguments","--","--","--","--","-","mha_left_context_size",":","int","How","many","left","frames","should","be","saved","and","used","as","left","context","to","the","current","chunk","when","streaming"]
code: def make_streaming_context(self, mha_left_context_size: int):
      """Creates a blank streaming context for this encoding layer.
  
      Arguments
      ---------
      mha_left_context_size : int
          How many left frames should be saved and used as left context to the
          current chunk when streaming
      """
      return ConformerEncoderLayerStreamingContext(
          mha_left_context_size=mha_left_context_size
      )
  
imports: from dataclasses import dataclass
  import torch
  import torch.nn
  import torch.nn.functional
  from typing import Optional, List
  import speechbrain
  import warnings
  from speechbrain.nnet.attention import RelPosMHAXL, MultiheadAttention, PositionalwiseFeedForward
  from speechbrain.utils.dynamic_chunk_training import DynChunkTrainConfig
  from speechbrain.lobes.models.transformer.hypermixing import HyperMixing
  from speechbrain.nnet.normalization import LayerNorm
  from speechbrain.nnet.activations import Swish
label_desc: Use image node_id 4 for calling the ConformerEncoderLayer obj's underlying member method code with example usage: obj.make_streaming_context(mha_left_context_size) and returns: ConformerEncoderLayerStreamingContext