class_docstr: Stack the input data samples to construct the batch.
  The N input samples must have the same shape/length and will be stacked to construct a batch.
class_docstr_tok: ["Stack","the","input","data","samples","to","construct","the","batch",".","The","N","input","samples","must","have","the","same","shape\/length","and","will","be","stacked","to","construct","a","batch","."]
docstr: Collate the input data.
  
  Parameters
  ----------
      data (list): The input data samples.
  
  Returns
  -------
      batch_data (torch.Tensor)
docstr_tok: ["Collate","the","input","data",".","Parameters","--","--","--","--","--","data","(","list",")",":","The","input","data","samples",".","Returns","--","--","--","-","batch_data","(","torch.Tensor",")"]
code: def __call__(self, data):
      """
      Collate the input data.
  
      Parameters
      ----------
          data (list): The input data samples.
  
      Returns
      -------
          batch_data (torch.Tensor)
      """
      return _stack_arrs(data)
  
file_path: autogluon/multimodal/src/autogluon/multimodal/data/collator.py
imports: import math
  import warnings
  import numpy
  import torch
label_desc: Use image node_id 1 for calling the StackCollator obj's underlying member method code with example usage: obj.__call__(data) and returns: _stack_arrs