class_docstr: Merge patches by taking average of the overlapping area
  
  Args:
      merged_shape: the shape of the tensor required to merge the patches.
      cropped_shape: the shape of the final merged output tensor.
          If not provided, it will be the same as `merged_shape`.
      device: the device for aggregator tensors and final results.
      value_dtype: the dtype for value aggregating tensor and the final result.
      count_dtype: the dtype for sample counting tensor.
docstr: Get the aggregator tensor for number of samples.
  
  Returns:
      torch.Tensor: number of accumulated samples at each location.
docstr_tok: ["Get","the","aggregator","tensor","for","number","of","samples",".","Returns",":","torch.Tensor",":","number","of","accumulated","samples","at","each","location","."]
code: def get_counts(self) -> torch.Tensor:
      """
      Get the aggregator tensor for number of samples.
  
      Returns:
          torch.Tensor: number of accumulated samples at each location.
      """
      return self.counts
  
imports: from __future__ import annotations
  import threading
  from abc import ABC, abstractmethod
  from collections.abc import Sequence
  from contextlib import nullcontext
  from typing import TYPE_CHECKING, Any
  import numpy
  import torch
  from monai.utils import ensure_tuple_size, optional_import, require_pkg
label_desc: Use image node_id 6 for calling the AvgMerger obj's underlying member method code with example usage: obj.get_counts() and returns: self