docstr: Perform padding on a list of arrays so that they each have the same
  length, by taking the maximum dimension across each axis. This only
  works on non-empty sequences with the same `ndim` and `dtype`.
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code: def pad(self, seqs, round_to=1):
      """Perform padding on a list of arrays so that they each have the same
      length, by taking the maximum dimension across each axis. This only
      works on non-empty sequences with the same `ndim` and `dtype`.
      """
      # TODO: This should be generalized to handle different ranks
      if not seqs:
          raise ValueError("Cannot pad empty sequence")
      if len(set(seq.ndim for seq in seqs)) != 1:
          raise ValueError("Cannot pad sequences with different ndims")
      if len(set(seq.dtype for seq in seqs)) != 1:
          raise ValueError("Cannot pad sequences with different dtypes")
      if len(set(seq.shape[1:] for seq in seqs)) != 1:
          raise ValueError(
              "Cannot pad sequences that differ on other dimensions"
          )
  
      # Our CUDA kernel can currently only handle C contiguous arrays.
      if not all(seq.flags["C_CONTIGUOUS"] for seq in seqs) or seqs[
          0
      ].dtype not in (
          "float32",
          "float64",
          "int32",
          "int64",
      ):
          return super().pad(seqs, round_to)
  
      return _custom_kernels.pad(seqs, round_to)
  
imports: import numpy
  from ..None import registry
  from ..compat import cupy, cupyx
  from ..types import DeviceTypes
  from ..util import is_cupy_array, is_mxnet_gpu_array, is_tensorflow_gpu_array, is_torch_cuda_array, mxnet2xp, tensorflow2xp, torch2xp
  from .None import _custom_kernels
  from .numpy_ops import NumpyOps
  from .ops import Ops
label_desc: Use image node_id 10 for calling the CupyOps obj's underlying member method code with example usage: obj.pad(seqs, round_to) and returns: _custom_kernels, super