class_docstr: Fake layer to test runtime and memory benchmarking.
  
  Kwargs:
      runtime: duration for one forward or backward pass
      pass_memory: memory used during a forward or backward pass
      layer_memory: memory taken up by the module
class_docstr_tok: ["Fake","layer","to","test","runtime","and","memory","benchmarking",".","Kwargs",":","runtime",":","duration","for","one","forward","or","backward","pass","pass_memory",":","memory","used","during","a","forward","or","backward","pass","layer_memory",":","memory","taken","up","by","the","module"]
code: def __init__(self, **kwargs) -> None:
      self._runtime = kwargs.get("runtime", 0)
      self._pass_memory = kwargs.get("pass_memory", 0)
      self._input_tensor = get_n_byte_tensor(0)
      self._module = FakeModule(**kwargs)
      self._labels = get_n_byte_tensor(0)
  
file_path: opacus/benchmarks/tests/test_benchmark_layer.py
imports: import math
  import time
  import pytest
  import torch
  import torch.nn
  from helpers import get_actual_memory_allocated, get_n_byte_tensor, skipifnocuda
  from benchmarks.benchmark_layer import run_layer_benchmark
  from benchmarks.layers import Layer
  from benchmarks.utils import reset_peak_memory_stats
label_desc: Use image node_id 1 to create a new FakeLayer object from inherited base classes: Layer with example: obj = FakeLayer()