parameters: (self)
docstring: Builds block according to the arguments.
docstring_summary: Builds block according to the arguments.
docstring_tokens: Builds block according to the arguments .
function: def _build(self):
          """Builds block according to the arguments."""
          filters = self._block_args.input_filters * self._block_args.expand_ratio
          # pylint: disable=g-long-lambda
          cid = itertools.count(0)
          get_conv_name = lambda: "conv2d" + (
              "" if not next(cid) else "_" + str(next(cid) // 2)
          )
          # pylint: enable=g-long-lambda
          kernel_size = self._block_args.kernel_size
          if self._block_args.expand_ratio != 1:
              # Expansion phase:
              self._expand_conv = tf.keras.layers.Conv2D(
                  filters,
                  kernel_size=[kernel_size, kernel_size],
                  strides=[1, 1],
                  kernel_initializer=conv_kernel_initializer,
                  padding="same",
                  use_bias=False,
                  name=get_conv_name(),
              )
              self._bn0 = self._batch_norm(
                  axis=self._channel_axis,
                  momentum=self._batch_norm_momentum,
                  epsilon=self._batch_norm_epsilon,
              )
  
          # Output phase:
          filters = self._block_args.output_filters
          self._project_conv = tf.keras.layers.Conv2D(
              filters,
              kernel_size=[1, 1],
              strides=self._block_args.strides,
              kernel_initializer=conv_kernel_initializer,
              padding="same",
              use_bias=False,
              name=get_conv_name(),
          )
          self._bn1 = self._batch_norm(
              axis=self._channel_axis,
              momentum=self._batch_norm_momentum,
              epsilon=self._batch_norm_epsilon,
          )
function_tokens: def _build ( self ) : filters = self . _block_args . input_filters * self . _block_args . expand_ratio # pylint: disable=g-long-lambda cid = itertools . count ( 0 ) get_conv_name = lambda : "conv2d" + ( "" if not next ( cid ) else "_" + str ( next ( cid ) // 2 ) ) # pylint: enable=g-long-lambda kernel_size = self . _block_args . kernel_size if self . _block_args . expand_ratio != 1 : # Expansion phase: self . _expand_conv = tf . keras . layers . Conv2D ( filters , kernel_size = [ kernel_size , kernel_size ] , strides = [ 1 , 1 ] , kernel_initializer = conv_kernel_initializer , padding = "same" , use_bias = False , name = get_conv_name ( ) , ) self . _bn0 = self . _batch_norm ( axis = self . _channel_axis , momentum = self . _batch_norm_momentum , epsilon = self . _batch_norm_epsilon , ) # Output phase: filters = self . _block_args . output_filters self . _project_conv = tf . keras . layers . Conv2D ( filters , kernel_size = [ 1 , 1 ] , strides = self . _block_args . strides , kernel_initializer = conv_kernel_initializer , padding = "same" , use_bias = False , name = get_conv_name ( ) , ) self . _bn1 = self . _batch_norm ( axis = self . _channel_axis , momentum = self . _batch_norm_momentum , epsilon = self . _batch_norm_epsilon , )