class_docstr: A single block of the Squeezeformer encoder.
  
  Args:
      d_model (int): input dimension of MultiheadAttentionMechanism and PositionwiseFeedForward
      d_ff (int): hidden dimension of PositionwiseFeedForward
      n_heads (int): number of heads for multi-head attention
      conv_kernel_size (int): kernel size for depthwise convolution in convolution module
      dropout (float): dropout probabilities for linear layers
      dropout_att (float): dropout probabilities for attention distributions
      adaptive_scale (bool): Whether to scale the inputs to each component by affine `scale` and `bias` layer.
          Or use a fixed scale=1 and bias=0.
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code: def reset_parameters(self):
      # Used for Squeezeformer initialization only
      self.feed_forward1.reset_parameters_ff()
      self.feed_forward2.reset_parameters_ff()
      self.conv.reset_parameters_conv()
  
imports: import torch
  from torch import nn
  from torch.nn import LayerNorm
  from nemo.collections.asr.parts.submodules.conformer_modules import ConformerConvolution, ConformerFeedForward
  from nemo.collections.asr.parts.submodules.multi_head_attention import MultiHeadAttention, RelPositionMultiHeadAttention
  from nemo.collections.common.parts import adapter_modules
  from nemo.core.classes.mixins import AccessMixin
  from nemo.core.classes.mixins.adapter_mixins import AdapterModuleMixin
label_desc: Use image node_id 4 for calling the SqueezeformerLayer obj's underlying member method code with example usage: obj.reset_parameters() without return types