class_docstr: Dataset class for loading in-memory data.
  
  :param inputs: (numpy array)
  
  :param targets: (numpy array)
  
  :param input_transform: (transform):
      transform to apply to input sample individually
  
  :param target_transform: (transform):
      transform to apply to target sample individually
  
  :param co_transform: (transform):
      transform to apply to both input and target sample simultaneously
class_docstr_tok: ["Dataset","class","for","loading","in-memory","data",".",":","param","inputs",":","(","numpy","array",")",":","param","targets",":","(","numpy","array",")",":","param","input_transform",":","(","transform",")",":","transform","to","apply","to","input","sample","individually",":","param","target_transform",":","(","transform",")",":","transform","to","apply","to","target","sample","individually",":","param","co_transform",":","(","transform",")",":","transform","to","apply","to","both","input","and","target","sample","simultaneously"]
code: def __init__(
      self,
      inputs,
      targets=None,
      input_transform=None,
      target_transform=None,
      co_transform=None,
  ):
      self.inputs = _process_array_argument(inputs)
      self.num_inputs = len(self.inputs)
      self.input_return_processor = (
          _return_first_element_of_list
          if self.num_inputs == 1
          else _pass_through
      )
  
      if targets is None:
          self.has_target = False
      else:
          self.targets = _process_array_argument(targets)
          self.num_targets = len(self.targets)
          self.target_return_processor = (
              _return_first_element_of_list
              if self.num_targets == 1
              else _pass_through
          )
          self.min_inputs_or_targets = min(
              self.num_inputs, self.num_targets
          )
          self.has_target = True
  
      self.input_transform = _process_transform_argument(
          input_transform, self.num_inputs
      )
      if self.has_target:
          self.target_transform = _process_transform_argument(
              target_transform, self.num_targets
          )
          self.co_transform = _process_co_transform_argument(
              co_transform, self.num_inputs, self.num_targets
          )
  
imports: from .BaseDataset import BaseDataset
  from .data_utils import _process_array_argument, _return_first_element_of_list, _process_transform_argument, _process_co_transform_argument, _pass_through
label_desc: Use image node_id 1 to create a new TensorDataset object from inherited base classes: BaseDataset with example: obj = TensorDataset(inputs, targets, input_transform, target_transform, co_transform)