class_docstr: feed-forward module of Conformer model.
class_docstr_tok: ["feed-forward","module","of","Conformer","model","."]
code: def __init__(self, d_model, d_ff, dropout, activation=Swish()):
      super(ConformerFeedForward, self).__init__()
      self.d_model = d_model
      self.d_ff = d_ff
      self.linear1 = nn.Linear(d_model, d_ff)
      self.activation = activation
      self.dropout = nn.Dropout(p=dropout)
      self.linear2 = nn.Linear(d_ff, d_model)
  
imports: import torch
  from torch import nn
  from torch.nn import LayerNorm
  from nemo.collections.asr.parts.submodules.batchnorm import FusedBatchNorm1d
  from nemo.collections.asr.parts.submodules.causal_convs import CausalConv1D
  from nemo.collections.asr.parts.submodules.multi_head_attention import MultiHeadAttention, RelPositionMultiHeadAttention, RelPositionMultiHeadAttentionLongformer
  from nemo.collections.asr.parts.utils.activations import Swish
  from nemo.collections.common.parts import adapter_modules
  from nemo.collections.common.parts.utils import activation_registry
  from nemo.core.classes.mixins import AccessMixin
  from nemo.core.classes.mixins.adapter_mixins import AdapterModuleMixin
label_desc: Use image node_id 1 to create a new ConformerFeedForward object from inherited base classes: nn with example: obj = ConformerFeedForward(d_model, d_ff, dropout, activation)