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: Call supported layers
docstr_tok: ["Call","supported","layers"]
code: def _call_layer(self, layer):
      """Call supported layers"""
      if layer == "conv1d":
          x, x_enc, y, y_enc = self.random_conv1d_inputs()
      elif layer == "conv2d":
          x, x_enc, y, y_enc = self.random_conv2d_inputs()
      else:
          raise ValueError(f"{layer} not supported")
  
      ref = getattr(torch.nn.functional, layer)(x, y)
      out_enc = getattr(x_enc, layer)(y_enc)
  
      return ref, out_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 12 for calling the FuncBenchmarks obj's underlying member method code with example usage: obj._call_layer(layer) and returns: ref,  out_enc