class_docstr: Build Trunk Blocks for Efficient/Mobile Networks
  
  This ended up being somewhat of a cross between
  https://github.com/tensorflow/tpu/blob/master/models/official/mnasnet/mnasnet_models.py
  and
  https://github.com/facebookresearch/maskrcnn-benchmark/blob/master/maskrcnn_benchmark/modeling/backbone/fbnet_builder.py
class_docstr_tok: ["Build","Trunk","Blocks","for","Efficient\/Mobile","Networks","This","ended","up","being","somewhat","of","a","cross","between","https",":","\/\/github.com\/tensorflow\/tpu\/blob\/master\/models\/official\/mnasnet\/mnasnet_models.py","and","https",":","\/\/github.com\/facebookresearch\/maskrcnn-benchmark\/blob\/master\/maskrcnn_benchmark\/modeling\/backbone\/fbnet_builder.py"]
code: def _make_stack(self, stack_args):
      blocks = []
      # each stack (stage) contains a list of block arguments
      for i, ba in enumerate(stack_args):
          if i >= 1:
              # only the first block in any stack can have a stride > 1
              ba["stride"] = 1
          block = self._make_block(ba)
          blocks.append(block)
          self.block_idx += 1  # incr global idx (across all stacks)
      return nn.Sequential(*blocks)
  
imports: import re
  from copy import deepcopy
  from .conv2d_layers import *
  from geffnet.activations import *
label_desc: Use image node_id 4 for calling the EfficientNetBuilder obj's underlying member method code with example usage: obj._make_stack(stack_args) and returns: nn