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: Applies layers in sequence, passing only the first element of tuples.
  
  Arguments
  ---------
  x : torch.Tensor
      The input tensor to run through the network.
docstr_tok: ["Applies","layers","in","sequence",",","passing","only","the","first","element","of","tuples",".","Arguments","--","--","--","--","-","x",":","torch.Tensor","The","input","tensor","to","run","through","the","network","."]
code: def forward(self, x):
      """Applies layers in sequence, passing only the first element of tuples.
  
      Arguments
      ---------
      x : torch.Tensor
          The input tensor to run through the network.
      """
      for layer in self.values():
          x = layer(x)
          if isinstance(x, tuple):
              x = x[0]
  
      return x
  
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 4 for calling the Sequential obj's underlying member method code with example usage: obj.forward(x) and returns: x