code: def __init__(
      self,
      input_dim=10,
      output_dim=10,
      total_samples=100,
  ):
      self.input_dim = input_dim
      self.output_dim = output_dim
      self.total_samples = total_samples
      self.input_samples = torch.rand(
          self.total_samples, self.input_dim, self.output_dim
      )
      self.target_samples = torch.rand(
          self.total_samples, self.input_dim, self.output_dim
      )
  
imports: import torch
  from torch import nn
  from torch.optim.optimizer import Optimizer
  from torch.utils.data import DataLoader, Dataset
  from fairscale.experimental.nn.ampnet_pipe.pipe import AMPnetPipe
  from fairscale.fair_dev.testing.testing import get_worker_map, torch_spawn
label_desc: Use image node_id 1 to create a new FakeDataset object from inherited base classes: Dataset with example: obj = FakeDataset(input_dim, output_dim, total_samples)