class_docstr: A sequence of modules with potentially inferring shape on construction.
  
  If layers are passed with names, these can be referenced with dot notation.
  
  Arguments
  ---------
  input_shape : iterable
      A list or tuple of ints or None, representing the expected shape of an
      input tensor. None represents a variable-length dimension. If no
      ``input_shape`` is passed, no shape inference will be performed.
  *layers, **named_layers
      The inputs are treated as a list of layers to be
      applied in sequence. The output shape of each layer is used to
      infer the shape of the following layer. If a tuple is returned,
      only the shape of the first element is used to determine input
      shape of the next layer (e.g. RNN returns output, hidden).
  
  Example
  -------
  >>> inputs = torch.rand(10, 40, 50)
  >>> model = Sequential(input_shape=inputs.shape)
  >>> model.append(Linear, n_neurons=100, layer_name="layer1")
  >>> model.append(Linear, n_neurons=200, layer_name="layer2")
  >>> outputs = model(inputs)
  >>> outputs.shape
  torch.Size([10, 40, 200])
  >>> outputs = model.layer1(inputs)
  >>> outputs.shape
  torch.Size([10, 40, 100])
docstr: Returns expected shape of the output.
  
  Computed by passing dummy input constructed with the
  ``self.input_shape`` attribute.
docstr_tok: ["Returns","expected","shape","of","the","output",".","Computed","by","passing","dummy","input","constructed","with","the","``","self.input_shape","``","attribute","."]
code: def get_output_shape(self):
      """Returns expected shape of the output.
  
      Computed by passing dummy input constructed with the
      ``self.input_shape`` attribute.
      """
      with torch.no_grad():
          dummy_input = torch.zeros(self.input_shape)
          dummy_output = self(dummy_input)
      return dummy_output.shape
  
imports: import torch
  import inspect
  import logging
  import operator
  import functools
  from speechbrain.nnet.linear import Linear
  from speechbrain.utils.callchains import lengths_arg_exists
label_desc: Use image node_id 3 for calling the Sequential obj's underlying member method code with example usage: obj.get_output_shape() and returns: dummy_output