code: def make_base_shapes(base_shapes, delta_shapes, savefile=None):
      """Make a base shape object from a base model/shapes and a delta model/shapes.
  
      Inputs:
          base:
              a base `nn.Module` or a dict of shapes
          delta:
              a "delta" model or a dict of shapes, for the sole purpose of
              determining which dimensions are "width" and will be scaled up and
              down in the target model.
          savefile:
              if a string, then the resulting base shape object is serialized to
              this location via yaml encoding.
      Outputs:
          base infshapes
      """
      bsh = clear_dims(zip_infshapes(base_shapes, delta_shapes))
      if savefile is not None:
          save_base_shapes(bsh, savefile)
      return bsh
  
file_path: NeMo/nemo/collections/nlp/modules/common/megatron/mup/shape.py
imports: from copy import deepcopy
  import yaml
  from torch import nn
  from torch.nn import Linear
  from torch.nn.modules.conv import _ConvNd
  from nemo.collections.nlp.modules.common.megatron.mup.infshape import InfShape, zip_infshape
  from nemo.collections.nlp.modules.common.megatron.mup.layer import MuReadout, rescale_linear_bias
label_desc: Use image node_id 10 for calling a global function with example usage: make_base_shapes(base_shapes, delta_shapes, savefile) and returns: bsh