code: def allocate_query(
      self,
      memory_allocation: AllocationQuery,
      batch_size: int,
      batches_ahead: int,
  ):
      # We compute the total amount of memory needed for this
      # operation
      final_shape = [
          batches_ahead,
          batch_size,
          *memory_allocation.shape,
      ]
      if isinstance(memory_allocation.dtype, ch.dtype):
          result = []
          for _ in range(final_shape[0]):
              partial = ch.empty(
                  *final_shape[1:],
                  dtype=memory_allocation.dtype,
                  device=memory_allocation.device,
              )
              try:
                  partial = partial.pin_memory()
              except:
                  pass
              result.append(partial)
      else:
          ch_dtype = ch.from_numpy(
              np.empty(0, dtype=memory_allocation.dtype)
          ).dtype
          result = ch.empty(*final_shape, dtype=ch_dtype)
          try:
              result = result.pin_memory()
          except:
              pass
          result = result.numpy()
      return result
  
file_path: ffcv/ffcv/pipeline/pipeline.py
imports: from typing import Any, Optional, Sequence, Mapping
  import torch
  import numpy
  from .state import State
  from .operation import Operation
  from .allocation_query import Allocation, AllocationQuery
label_desc: Use image node_id 4 for calling the Pipeline obj's underlying member method code with example usage: obj.allocate_query(memory_allocation, batch_size, batches_ahead) and returns: result