class_docstr: NASNet Cell class that is used as a 'layer' in image architectures.
class_docstr_tok: ["NASNet","Cell","class","that","is","used","as","a","'layer","'","in","image","architectures","."]
docstr: Runs the conv cell.
docstr_tok: ["Runs","the","conv","cell","."]
code: def __call__(
      self,
      net,
      scope=None,
      filter_scaling=1,
      stride=1,
      prev_layer=None,
      cell_num=-1,
      current_step=None,
  ):
      """Runs the conv cell."""
      self._cell_num = cell_num
      self._filter_scaling = filter_scaling
      self._filter_size = int(self._num_conv_filters * filter_scaling)
  
      i = 0
      with tf.variable_scope(scope):
          net = self._cell_base(net, prev_layer)
          for iteration in range(5):
              with tf.variable_scope("comb_iter_{}".format(iteration)):
                  left_hiddenstate_idx, right_hiddenstate_idx = (
                      self._hiddenstate_indices[i],
                      self._hiddenstate_indices[i + 1],
                  )
                  original_input_left = left_hiddenstate_idx < 2
                  original_input_right = right_hiddenstate_idx < 2
                  h1 = net[left_hiddenstate_idx]
                  h2 = net[right_hiddenstate_idx]
  
                  operation_left = self._operations[i]
                  operation_right = self._operations[i + 1]
                  i += 2
                  # Apply conv operations
                  with tf.variable_scope("left"):
                      h1 = self._apply_conv_operation(
                          h1,
                          operation_left,
                          stride,
                          original_input_left,
                          current_step,
                      )
                  with tf.variable_scope("right"):
                      h2 = self._apply_conv_operation(
                          h2,
                          operation_right,
                          stride,
                          original_input_right,
                          current_step,
                      )
  
                  # Combine hidden states using 'add'.
                  with tf.variable_scope("combine"):
                      h = h1 + h2
                      if self._use_bounded_activation:
                          h = tf.nn.relu6(h)
  
                  # Add hiddenstate to the list of hiddenstates we can choose from
                  net.append(h)
  
          with tf.variable_scope("cell_output"):
              net = self._combine_unused_states(net)
  
          return net
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow.compat.v1
label_desc: Use image node_id 4 for calling the NasNetABaseCell obj's underlying member method code with example usage: obj.__call__(net, scope, filter_scaling, stride, prev_layer, cell_num, current_step) and returns: net