class_docstr: Implements the Numpy backend ops for a PC auto-batching VM.
class_docstr_tok: ["Implements","the","Numpy","backend","ops","for","a","PC","auto-batching","VM","."]
docstr: Merges two dtypes, returning a compatible dtype.
  
  Args:
    dt1: A numpy dtype, or None.
    dt2: A numpy dtype, or None.
  
  Returns:
    dtype: The more precise numpy dtype (e.g. prefers int64 over int32).
docstr_tok: ["Merges","two","dtypes",",","returning","a","compatible","dtype",".","Args",":","dt1",":","A","numpy","dtype",",","or","None",".","dt2",":","A","numpy","dtype",",","or","None",".","Returns",":","dtype",":","The","more","precise","numpy","dtype","(","e.g",".","prefers","int64","over","int32",")","."]
code: def merge_dtypes(self, dt1, dt2):
      """Merges two dtypes, returning a compatible dtype.
  
      Args:
        dt1: A numpy dtype, or None.
        dt2: A numpy dtype, or None.
  
      Returns:
        dtype: The more precise numpy dtype (e.g. prefers int64 over int32).
      """
      return (np.zeros([], dtype=dt1) + np.zeros([], dtype=dt2)).dtype
  
imports: import collections
  import numpy
  from tensorflow_probability.python.experimental.auto_batching import instructions
label_desc: Use image node_id 4 for calling the NumpyBackend obj's underlying member method code with example usage: obj.merge_dtypes(dt1, dt2) and returns: unknown