original_docstring: Build the graph for this configuration.
  
         Args:
           inputs: input node (already preprocessed)
  
         Returns:
           A dict of outputs that includes the 'predictions', 'loss', the 'encoding',
           the 'quantized_input', and whatever metrics we want to track for eval.
         
docstring: Build the graph for this configuration.
docstring_tokens: Build the graph for this configuration .
code: def _build(self, inputs):
          """Build the graph for this configuration.
  
         Args:
           inputs: input node (already preprocessed)
  
         Returns:
           A dict of outputs that includes the 'predictions', 'loss', the 'encoding',
           the 'quantized_input', and whatever metrics we want to track for eval.
         """
  
          # https://deepmind.github.io/sonnet/_modules/sonnet/python/modules/conv.html#Conv1D
  
          ###
          # The Non-Causal Temporal Encoder.
          ###
          ae_startconv = snt.Conv1D(output_channels=self._e_hidden_channels,
                                    kernel_shape=self._filter_length,
                                    name='ae_startconv')
  
          en = ae_startconv(inputs)
  
          for num_layer in range(self._num_layers):
              dilation = 2 ** (num_layer % self._num_layers_per_stage)
              d = tf.nn.relu(en)
  
              ae_dilatedconv = snt.Conv1D(output_channels=self._e_hidden_channels,
                                          kernel_shape=self._filter_length,
                                          rate=dilation,
                                          name='ae_dilatedconv_%d' % (num_layer + 1))
  
              d = ae_dilatedconv(d)
  
              d = tf.nn.relu(d)
  
              ae_res = snt.Conv1D(output_channels=self._e_hidden_channels,
                                  kernel_shape=1,
                                  padding=snt.CAUSAL,
                                  name='ae_res_%d' % (num_layer + 1))
  
              en += ae_res(d)
  
          ae_bottleneck = snt.Conv1D(output_channels=self._latent_channels,
                                     kernel_shape=1,
                                     padding=snt.CAUSAL,
                                     name='ae_bottleneck')
  
          en = ae_bottleneck(en)
  
          return en
code_tokens: def _build ( self , inputs ) : ae_startconv = snt . Conv1D ( output_channels = self . _e_hidden_channels , kernel_shape = self . _filter_length , name = 'ae_startconv' ) en = ae_startconv ( inputs ) for num_layer in range ( self . _num_layers ) : dilation = 2 ** ( num_layer % self . _num_layers_per_stage ) d = tf . nn . relu ( en ) ae_dilatedconv = snt . Conv1D ( output_channels = self . _e_hidden_channels , kernel_shape = self . _filter_length , rate = dilation , name = 'ae_dilatedconv_%d' % ( num_layer + 1 ) ) d = ae_dilatedconv ( d ) d = tf . nn . relu ( d ) ae_res = snt . Conv1D ( output_channels = self . _e_hidden_channels , kernel_shape = 1 , padding = snt . CAUSAL , name = 'ae_res_%d' % ( num_layer + 1 ) ) en += ae_res ( d ) ae_bottleneck = snt . Conv1D ( output_channels = self . _latent_channels , kernel_shape = 1 , padding = snt . CAUSAL , name = 'ae_bottleneck' ) en = ae_bottleneck ( en ) return en
short_docstring: Build the graph for this configuration.
short_docstring_tokens: Build the graph for this configuration .
comment: """Build the graph for this configuration.
  
         Args:
           inputs: input node (already preprocessed)
  
         Returns:
           A dict of outputs that includes the 'predictions', 'loss', the 'encoding',
           the 'quantized_input', and whatever metrics we want to track for eval.
         """$SEP$# https://deepmind.github.io/sonnet/_modules/sonnet/python/modules/conv.html#Conv1D$SEP$###$SEP$# The Non-Causal Temporal Encoder.$SEP$###