class_docstr: NASNet Cell class that is used as a 'layer' in image architectures.
  
  Args:
    num_conv_filters: The number of filters for each convolution operation.
    operations: List of operations that are performed in the NASNet Cell in
      order.
    used_hiddenstates: Binary array that signals if the hiddenstate was used
      within the cell. This is used to determine what outputs of the cell
      should be concatenated together.
    hiddenstate_indices: Determines what hiddenstates should be combined
      together with the specified operations to create the NASNet cell.
class_docstr_tok: ["NASNet","Cell","class","that","is","used","as","a","'layer","'","in","image","architectures",".","Args",":","num_conv_filters",":","The","number","of","filters","for","each","convolution","operation",".","operations",":","List","of","operations","that","are","performed","in","the","NASNet","Cell","in","order",".","used_hiddenstates",":","Binary","array","that","signals","if","the","hiddenstate","was","used","within","the","cell",".","This","is","used","to","determine","what","outputs","of","the","cell","should","be","concatenated","together",".","hiddenstate_indices",":","Determines","what","hiddenstates","should","be","combined","together","with","the","specified","operations","to","create","the","NASNet","cell","."]
docstr: Runs the beginning of the conv cell before the predicted ops are run.
docstr_tok: ["Runs","the","beginning","of","the","conv","cell","before","the","predicted","ops","are","run","."]
code: def _cell_base(self, net, prev_layer):
      """Runs the beginning of the conv cell before the predicted ops are run."""
      num_filters = self._filter_size
  
      # Check to be sure prev layer stuff is setup correctly
      prev_layer = self._reduce_prev_layer(prev_layer, net)
  
      net = tf.nn.relu(net)
      net = slim.conv2d(net, num_filters, 1, scope="1x1")
      net = slim.batch_norm(net, scope="beginning_bn")
      split_axis = get_channel_index()
      net = tf.split(axis=split_axis, num_or_size_splits=1, value=net)
      for split in net:
          assert int(
              split.shape[split_axis]
              == int(self._num_conv_filters * self._filter_scaling)
          )
      net.append(prev_layer)
      return net
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow
label_desc: Use image node_id 3 for calling the NasNetABaseCell obj's underlying member method code with example usage: obj._cell_base(net, prev_layer) and returns: net