code: def get_output(layer_or_layers, inputs=None, **kwargs):
      """
      Computes the output of the network at one or more given layers.
      Optionally, you can define the input(s) to propagate through the network
      instead of using the input variable(s) associated with the network's
      input layer(s).
  
      Parameters
      ----------
      layer_or_layers : Layer or list
          the :class:`Layer` instance for which to compute the output
          expressions, or a list of :class:`Layer` instances.
  
      inputs : None, Theano expression, numpy array, or dict
          If None, uses the input variables associated with the
          :class:`InputLayer` instances.
          If a Theano expression, this defines the input for a single
          :class:`InputLayer` instance. Will throw a ValueError if there
          are multiple :class:`InputLayer` instances.
          If a numpy array, this will be wrapped as a Theano constant
          and used just like a Theano expression.
          If a dictionary, any :class:`Layer` instance (including the
          input layers) can be mapped to a Theano expression or numpy
          array to use instead of its regular output.
  
      Returns
      -------
      output : Theano expression or list
          the output of the given layer(s) for the given network input
  
      Notes
      -----
      Depending on your network architecture, `get_output([l1, l2])` may
      be crucially different from `[get_output(l1), get_output(l2)]`. Only
      the former ensures that the output expressions depend on the same
      intermediate expressions. For example, when `l1` and `l2` depend on
      a common dropout layer, the former will use the same dropout mask for
      both, while the latter will use two different dropout masks.
      """
      from .input import InputLayer
      from .base import MergeLayer, Layer
  
      # check if the keys of the dictionary are valid
      if isinstance(inputs, dict):
          for input_key in inputs.keys():
              if (input_key is not None) and (
                  not isinstance(input_key, Layer)
              ):
                  raise TypeError(
                      "The inputs dictionary keys must be"
                      " lasagne layers not %s." % type(input_key)
                  )
      # track accepted kwargs used by get_output_for
      accepted_kwargs = {"deterministic"}
      # obtain topological ordering of all layers the output layer(s) depend on
      treat_as_input = inputs.keys() if isinstance(inputs, dict) else []
      all_layers = get_all_layers(layer_or_layers, treat_as_input)
      # initialize layer-to-expression mapping from all input layers
      all_outputs = dict(
          (layer, layer.input_var)
          for layer in all_layers
          if isinstance(layer, InputLayer)
          and layer not in treat_as_input
      )
      # update layer-to-expression mapping from given input(s), if any
      if isinstance(inputs, dict):
          all_outputs.update(
              (layer, utils.as_theano_expression(expr))
              for layer, expr in inputs.items()
          )
      elif inputs is not None:
          if len(all_outputs) > 1:
              raise ValueError(
                  "get_output() was called with a single input "
                  "expression on a network with multiple input "
                  "layers. Please call it with a dictionary of "
                  "input expressions instead."
              )
          for input_layer in all_outputs:
              all_outputs[input_layer] = utils.as_theano_expression(
                  inputs
              )
      # update layer-to-expression mapping by propagating the inputs
      for layer in all_layers:
          if layer not in all_outputs:
              try:
                  if isinstance(layer, MergeLayer):
                      layer_inputs = [
                          all_outputs[input_layer]
                          for input_layer in layer.input_layers
                      ]
                  else:
                      layer_inputs = all_outputs[layer.input_layer]
              except KeyError:
                  # one of the input_layer attributes must have been `None`
                  raise ValueError(
                      "get_output() was called without giving an "
                      "input expression for the free-floating "
                      "layer %r. Please call it with a dictionary "
                      "mapping this layer to an input expression."
                      % layer
                  )
              all_outputs[layer] = layer.get_output_for(
                  layer_inputs, **kwargs
              )
              try:
                  accepted_kwargs |= set(
                      utils.inspect_kwargs(layer.get_output_for)
                  )
              except TypeError:
                  # If introspection is not possible, skip it
                  pass
              accepted_kwargs |= set(layer.get_output_kwargs)
      unused_kwargs = set(kwargs.keys()) - accepted_kwargs
      if unused_kwargs:
          suggestions = []
          for kwarg in unused_kwargs:
              suggestion = get_close_matches(kwarg, accepted_kwargs)
              if suggestion:
                  suggestions.append(
                      "%s (perhaps you meant %s)"
                      % (kwarg, suggestion[0])
                  )
              else:
                  suggestions.append(kwarg)
          warn(
              "get_output() was called with unused kwargs:\n\t%s"
              % "\n\t".join(suggestions)
          )
      # return the output(s) of the requested layer(s) only
      try:
          return [all_outputs[layer] for layer in layer_or_layers]
      except TypeError:
          return all_outputs[layer_or_layers]
  
file_path: Lasagne/lasagne/layers/helper.py
imports: from collections import deque
  from difflib import get_close_matches
  from itertools import chain
  from warnings import warn
  import theano
  import numpy
  from ..None import utils
label_desc: Use image node_id 2 for calling a global function with example usage: get_output(layer_or_layers, inputs) and returns: unknown, all_outputs