class_docstr: Indicates that a parameter should alias the value of another parameter.
  
  When used in conjunction with a custom distribution, this allows fuzzed
  tensors to represent a broader range of behaviors. For example, the
  following sometimes produces Tensors which broadcast:
  
  Fuzzer(
      parameters=[
          FuzzedParameter("x_len", 4, 1024, distribution="uniform"),
  
          # `y` will either be size one, or match the size of `x`.
          FuzzedParameter("y_len", distribution={
              0.5: 1,
              0.5: ParameterAlias("x_len")
          }),
      ],
      tensors=[
          FuzzedTensor("x", size=("x_len",)),
          FuzzedTensor("y", size=("y_len",)),
      ],
  )
  
  Chains of alias' are allowed, but may not contain cycles.
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code: def __init__(self, alias_to):
      self.alias_to = alias_to
  
imports: import functools
  import itertools
  from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  import numpy
  import torch
label_desc: Use image node_id 1 to create a new ParameterAlias object  with example: obj = ParameterAlias(alias_to)