code: def calculate_out_dimension(d_in, module_instance, index):
      """
      For calculating h_in and w_out according to the conv2D documentation
      """
      padding = (
          (module_instance.padding, module_instance.padding)
          if isinstance(module_instance.padding, int)
          else module_instance.padding
      )
      kernel_size = (
          (module_instance.kernel_size, module_instance.kernel_size)
          if isinstance(module_instance.kernel_size, int)
          else module_instance.kernel_size
      )
      stride = (
          (module_instance.stride, module_instance.stride)
          if isinstance(module_instance.stride, int)
          else module_instance.stride
      )
      dilation = (
          (module_instance.dilation, module_instance.dilation)
          if isinstance(module_instance.dilation, int)
          else module_instance.dilation
      )
  
      DIMENSION_TYPES = (int, sympy.Symbol)
  
      if d_in == Dyn:
          return Dyn
  
      elif isinstance(d_in, DIMENSION_TYPES):
          n = (
              d_in
              + 2 * padding[index]
              - dilation[index] * (kernel_size[index] - 1)
              - 1
          )
  
          return (n // stride[0]) + 1
  
      else:
          raise TypeError(
              f"{d_in} in {module_instance} must be a number or Dyn. Received {type(d_in)}"
          )
  
imports: from functools import reduce
  import torch
  import operator
  from torch.fx.tensor_type import Dyn, is_consistent, TensorType, is_more_precise
  from typing import Callable, Dict
  from torch.fx.node import Target, Node
  from torch.nn.modules.batchnorm import BatchNorm2d
  from torch.nn.modules.conv import Conv2d
  from torch.fx.experimental.refinement_types import Equality
  import itertools
  from torch.fx.experimental.unification import Var
  import sympy
label_desc: Use image node_id 11 for calling a global function with example usage: calculate_out_dimension(d_in, module_instance, index) and returns: Dyn, unknown