class_docstr: This class implements the Squeeze-and-Excitation module.
  
  Arguments
  ---------
  inner_dim : int
      Inner dimension of bottle-neck network of the SE Module (default 12).
  activation : torch class
      Activation function for SE Module (default torch.nn.Sigmoid).
  norm : torch class
      Normalization to regularize the model (default BatchNorm1d).
  
  Example
  -------
  >>> inp = torch.randn([8, 120, 40])
  >>> net = SEmodule(input_shape=inp.shape, inner_dim=64)
  >>> out = net(inp)
  >>> out.shape
  torch.Size([8, 120, 40])
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docstr: Processes the input tensor x and returns an output tensor.
docstr_tok: ["Processes","the","input","tensor","x","and","returns","an","output","tensor","."]
code: def forward(self, x):
      """Processes the input tensor x and returns an output tensor."""
      bz, t, chn = x.shape
  
      x = self.conv(x)
      avg = self.avg_pool(x)
      avg = self.bottleneck(avg)
      context = avg.repeat(1, t, 1)
      return x * context
  
file_path: speechbrain/speechbrain/lobes/models/ContextNet.py
imports: import torch
  from torch.nn import Dropout
  from speechbrain.nnet.CNN import DepthwiseSeparableConv1d, Conv1d
  from speechbrain.nnet.linear import Linear
  from speechbrain.nnet.pooling import AdaptivePool
  from speechbrain.nnet.containers import Sequential
  from speechbrain.nnet.normalization import BatchNorm1d
  from speechbrain.nnet.activations import Swish
label_desc: Use image node_id 2 for calling the SEmodule obj's underlying member method code with example usage: obj.forward(x) and returns: unknown