docstr: Initializes a meta learning system dataloader. The data loader uses the Pytorch DataLoader class to parallelize
  batch sampling and preprocessing.
  
  Args:
      config
docstr_tok: ["Initializes","a","meta","learning","system","dataloader",".","The","data","loader","uses","the","Pytorch","DataLoader","class","to","parallelize","batch","sampling","and","preprocessing",".","Args",":","config"]
code: def __init__(
      self,
      num_training_tasks,
      training_batch_size,
      num_training_support,
      num_training_query,
      num_test_tasks,
      test_batch_size,
      num_test_support,
      num_test_query,
      amplitude,
      frequency,
      phase,
      x_range,
  ):
      """
      Initializes a meta learning system dataloader. The data loader uses the Pytorch DataLoader class to parallelize
      batch sampling and preprocessing.
  
      Args:
          config
  
      """
      self.training_dataset = MetaLearningDataSet(
          num_training_tasks,
          num_training_support,
          num_training_query,
          amplitude,
          frequency,
          phase,
          x_range,
      )
      self.test_dataset = MetaLearningDataSet(
          num_test_tasks,
          num_test_support,
          num_test_query,
          amplitude,
          frequency,
          phase,
          x_range,
      )
      self.training_batch_size = training_batch_size
      self.test_batch_size = test_batch_size
  
file_path: PARL/benchmark/torch/maml++/data.py
imports: import numpy
  from torch.utils.data import Dataset, DataLoader
  import torch
  import random
  import numpy
label_desc: Use image node_id 1 to create a new MetaLearningDataLoader object  with example: obj = MetaLearningDataLoader(num_training_tasks, training_batch_size, num_training_support, num_training_query, num_test_tasks, test_batch_size, num_test_support, num_test_query, amplitude, frequency, phase, x_range)