code: def uniformDistributionInit(self, low, high):
      # Initialize the memmap with a uniform distribution
      if self.CPUPinn == True:
          cupy.cuda.set_allocator(my_pinned_allocator)
  
      self._preInit()
      self.CUPYmemmap = cupy.load(
          self.fileName + ".cpy.npy", mmap_mode="r+"
      )
  
      if self.total_classes > 100000:
          for i in range(int(self.total_classes / 100000)):
              j = i * 100000
              self.CUPYmemmap[j : j + 100000] = cupy.random.uniform(
                  low=low,
                  high=high,
                  size=(100000, self.embed_dimension),
                  dtype=self.datatype,
              )
  
          for i in range(
              int(self.total_classes / 100000) * 100000,
              int(self.total_classes / 10000),
          ):
              j = i * 10000
              self.CUPYmemmap[j : j + 10000] = cupy.random.uniform(
                  low=low,
                  high=high,
                  size=(10000, self.embed_dimension),
                  dtype=self.datatype,
              )
  
          for i in range(
              int(self.total_classes / 10000) * 10000,
              self.total_classes,
          ):
              self.CUPYmemmap[i] = cupy.random.uniform(
                  low=low,
                  high=high,
                  size=(self.embed_dimension),
                  dtype=self.datatype,
              )
  
      elif self.total_classes > 10000:
          for i in range(int(self.total_classes / 10000)):
              j = i * 10000
              self.CUPYmemmap[j : j + 10000] = cupy.random.uniform(
                  low=low,
                  high=high,
                  size=(10000, self.embed_dimension),
                  dtype=self.datatype,
              )
  
          for i in range(
              int(self.total_classes / 10000) * 10000,
              self.total_classes,
          ):
              self.CUPYmemmap[i] = cupy.random.uniform(
                  low=low,
                  high=high,
                  size=(self.embed_dimension),
                  dtype=self.datatype,
              )
  
      else:
          for i in range(self.total_classes):
              self.CUPYmemmap[i] = cupy.random.uniform(
                  low=low,
                  high=high,
                  size=(self.embed_dimension),
                  dtype=self.datatype,
              )
  
      if self.CPUPinn == True:
          cupy.cuda.set_allocator(None)
  
imports: import numpy
  import cupy
  import torch
  import os
  from torch.utils.dlpack import to_dlpack
  from torch.utils.dlpack import from_dlpack
label_desc: Use image node_id 3 for calling the ModelFactoryMM obj's underlying member method code with example usage: obj.uniformDistributionInit(low, high) without return types