class_docstr: Benchmarks runtime and error of crypten functions against PyTorch
  
  Args:
      tensor_size (int or tuple): size of tensor for benchmarking runtimes
class_docstr_tok: ["Benchmarks","runtime","and","error","of","crypten","functions","against","PyTorch","Args",":","tensor_size","(","int","or","tuple",")",":","size","of","tensor","for","benchmarking","runtimes"]
docstr: Returns random input and weight tensors for 1d convolutions
docstr_tok: ["Returns","random","input","and","weight","tensors","for","1d","convolutions"]
code: def random_conv1d_inputs(self):
      """Returns random input and weight tensors for 1d convolutions"""
      size = self.tensor_size[0]
      filter_size = size // 10
      (x_conv1d,) = torch.rand(1, 1, size, device=self.device)
      weight1d = torch.rand(1, 1, filter_size, device=self.device)
      x_conv1d_enc = crypten.cryptensor(x_conv1d)
      weight1d_enc = crypten.cryptensor(weight1d)
      return x_conv1d, x_conv1d_enc, weight1d, weight1d_enc
  
imports: import argparse
  import functools
  import os
  import timeit
  from collections import namedtuple
  import crypten
  import crypten.communicator
  import numpy
  import pandas
  import torch
  from examples import multiprocess_launcher
label_desc: Use image node_id 7 for calling the FuncBenchmarks obj's underlying member method code with example usage: obj.random_conv1d_inputs() and returns: x_conv1d,  x_conv1d_enc,  weight1d,  weight1d_enc