class_docstr: Vision transformer as per https://arxiv.org/abs/2010.11929.
class_docstr_tok: ["Vision","transformer","as","per","https",":","\/\/arxiv.org\/abs\/2010.11929","."]
code: def get_params():
      return {
          "image_size": cfg.TRAIN.IM_SIZE,
          "patch_size": cfg.VIT.PATCH_SIZE,
          "stem_type": cfg.VIT.STEM_TYPE,
          "c_stem_kernels": cfg.VIT.C_STEM_KERNELS,
          "c_stem_strides": cfg.VIT.C_STEM_STRIDES,
          "c_stem_dims": cfg.VIT.C_STEM_DIMS,
          "n_layers": cfg.VIT.NUM_LAYERS,
          "n_heads": cfg.VIT.NUM_HEADS,
          "hidden_d": cfg.VIT.HIDDEN_DIM,
          "mlp_d": cfg.VIT.MLP_DIM,
          "cls_type": cfg.VIT.CLASSIFIER_TYPE,
          "num_classes": cfg.MODEL.NUM_CLASSES,
      }
  
imports: import math
  import numpy
  import torch
  from pycls.core.config import cfg
  from pycls.models.blocks import activation, conv2d, conv2d_cx, layernorm, layernorm_cx, linear, linear_cx, MultiheadAttention, norm2d, norm2d_cx, patchify2d, patchify2d_cx
  from torch.nn import init, Module, Parameter
label_desc: Use image node_id 1 for calling the ViT obj's underlying member method code with example usage: obj.get_params() and returns: dict