class_docstr: CrypTensor class that uses plaintext PyTorch tensors as underlying backend.
  This class should be used for testing purposes.
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.
docstr_tok: ["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","."]
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.
      """
      if device is None:
          device = torch.device("cpu")
      return PyTorchTensor(torch.rand(*sizes, device=device))
  
imports: import torch
  from crypten import CrypTensor, register_cryptensor
label_desc: Use image node_id 18 for calling the PyTorchTensor obj's underlying member method code with example usage: obj.rand() and returns: PyTorchTensor