code: def build_multi_input_dict(
      dims: Sequence[Sequence[int]], names: Sequence[str]
  ) -> Dict[str, torch.Tensor]:
      """build multi input dictionary"""
      input_dict = dict()
      for i, d in enumerate(dims):
          input_dict[names[i]] = build_input_tensor(d)
      return input_dict
  
file_path: maze/docs/source/policy_and_value_networks/code_snippets/inference_graph_example.py
imports: from typing import Sequence, Dict
  import torch
  from torch import nn
  from maze.perception.blocks.feed_forward.dense import DenseBlock
  from maze.perception.blocks.general.concat import ConcatenationBlock
  from maze.perception.blocks.inference import InferenceGraph, InferenceBlock
  from maze.perception.blocks.joint_blocks.vgg_conv_dense import VGGConvolutionDenseBlock
  from maze.perception.blocks.output.linear import LinearOutputBlock
  from maze.test.perception.perception_test_utils import build_input_tensor
label_desc: Use image node_id 1 for calling a global function with example usage: build_multi_input_dict(dims, names) and returns: input_dict