class_docstr: Abstract implementation of encrypted tensor type. Every subclass of `CrypTensor`
  must implement the methods defined here. The actual tensor data should live in
  an instance attribute called `_tensor`. When implemented, the `CrypTensor`
  provides a full autograd implementation to the user.
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docstr: Returns a tensor with elements uniformly sampled in [0, 1). The uniform
  random samples are generated by generating random bits using fixed-point
  encoding and converting the result to an ArithmeticSharedTensor.
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code: def rand(*sizes, device=None):
      """
      Returns a tensor with elements uniformly sampled in [0, 1). The uniform
      random samples are generated by generating random bits using fixed-point
      encoding and converting the result to an ArithmeticSharedTensor.
      """
      raise NotImplementedError("rand is not implemented")
  
imports: from contextlib import contextmanager
  import torch
  from .debug import register_validation
  from .gradients import AutogradContext, BaseAutogradContext, get_grad_fn
  from .common import functions
label_desc: Use image node_id 56 for calling the CrypTensor obj's underlying member method code with example usage: obj.rand() without return types