class_docstr: A sampling graph in CSC format.
class_docstr_tok: ["A","sampling","graph","in","CSC","format","."]
code: def _check_sampler_arguments(self, nodes, fanouts, probs_name):
      assert nodes.dim() == 1, "Nodes should be 1-D tensor."
      assert nodes.dtype == self.indices.dtype, (
          f"Data type of nodes must be consistent with "
          f"indices.dtype({self.indices.dtype}), but got {nodes.dtype}."
      )
      assert fanouts.dim() == 1, "Fanouts should be 1-D tensor."
      expected_fanout_len = 1
      if self.edge_type_to_id:
          expected_fanout_len = len(self.edge_type_to_id)
      assert len(fanouts) in [
          expected_fanout_len,
          1,
      ], "Fanouts should have the same number of elements as etypes or \
              should have a length of 1."
      if fanouts.size(0) > 1:
          assert (
              self.type_per_edge is not None
          ), "To perform sampling for each edge type (when the length of \
                  `fanouts` > 1), the graph must include edge type information."
      assert torch.all(
          (fanouts >= 0) | (fanouts == -1)
      ), "Fanouts should consist of values that are either -1 or \
              greater than or equal to 0."
      if probs_name:
          assert (
              probs_name in self.edge_attributes
          ), f"Unknown edge attribute '{probs_name}'."
          probs_or_mask = self.edge_attributes[probs_name]
          assert probs_or_mask.dim() == 1, "Probs should be 1-D tensor."
          assert (
              probs_or_mask.size(0) == self.total_num_edges
          ), "Probs should have the same number of elements as the number \
                  of edges."
          assert probs_or_mask.dtype in [
              torch.bool,
              torch.float16,
              torch.bfloat16,
              torch.float32,
              torch.float64,
          ], "Probs should have a floating-point or boolean data type."
  
imports: from typing import Dict, Optional, Union
  import torch
  from dgl.utils import recursive_apply
  from ...base import EID, ETYPE, NID, NTYPE
  from ...convert import to_homogeneous
  from ...heterograph import DGLGraph
  from ..base import etype_str_to_tuple, etype_tuple_to_str, ORIGINAL_EDGE_ID
  from ..sampling_graph import SamplingGraph
  from .sampled_subgraph_impl import CSCFormatBase, SampledSubgraphImpl
label_desc: Use image node_id 28 for calling the FusedCSCSamplingGraph obj's underlying member method code with example usage: obj._check_sampler_arguments(nodes, fanouts, probs_name) without return types