code: def get_all_ops_cfgs(dtype):
      return {
          "Convolution": {
              "data,kernel": [
                  (default_tensor(3, dtype), (3,)),
                  (default_tensor(4, dtype), (3, 3)),
                  (default_tensor(5, dtype), (3, 3, 3)),
              ],
              "weight": [TensorArg(common_weight_tensor)],
              "bias": [TensorArg(common_weight_tensor)],
              "no_bias": [False],
              "num_filter": [8],
          },
          "Deconvolution": {CFG_BASED_ON: "Convolution"},
          "FullyConnected": {
              "data": [default_tensor(2, dtype)],
              "weight": [TensorArg(common_weight_tensor)],
              "bias": [TensorArg(common_weight_tensor)],
              "num_hidden": [3],
          },
          "Pooling": {
              "data,kernel": [
                  (default_tensor(3, dtype), (3,)),
                  (default_tensor(4, dtype), (3, 3)),
                  (default_tensor(5, dtype), (3, 3, 3)),
              ],
          },
          "_contrib_AdaptiveAvgPooling2D": {
              "data,kernel,output_size": [
                  (default_tensor(4, dtype), (2, 2), (4, 4))
              ],
          },
          ######################################### Casting #########################################
          "Cast": {
              "data": [default_tensor(2, dtype)],
              "dtype": ["bool"],
          },
          "_contrib_quantize_v2": {
              "data": [default_tensor(2, dtype)],
              "min_calib_range": [
                  CfgBasedArg(lambda cfg: cfg["data"].min().asscalar())
              ],
              "max_calib_range": [
                  CfgBasedArg(lambda cfg: cfg["data"].max().asscalar())
              ],
              CFG_RTOL_ATOL: [(0, 0)],
          },
          ##################################### No calculations #####################################
          "Flatten": {"data": [default_tensor(2, dtype)]},
          "Concat": {
              "0,1,dim": [
                  (
                      default_tensor(2, dtype),
                      default_tensor(2, dtype),
                      0,
                  )
              ]
          },
          "Reshape": {"0": [default_tensor(2, dtype)], "1": [(-1,)]},
          "transpose": {"0": [default_tensor(2, dtype)]},
          "expand_dims": {
              "data": [default_tensor(2, dtype)],
              "axis": [-1],
          },
          "where": {
              "x,y,condition": [
                  (
                      default_tensor(2, dtype),
                      default_tensor(2, dtype),
                      mx.nd.random.randint(
                          0, 2, DEFAULT_SHAPE * 2, "int32"
                      ),
                  )
              ],
          },
          "take": {
              "a,indices": [
                  (
                      default_tensor(2, dtype),
                      mx.nd.random.randint(
                          0, DEFAULT_SHAPE[0], (2,), "int32"
                      ),
                  )
              ],
              "axis": [-1],
          },
          "stack": {
              "0,1": [
                  (default_tensor(2, dtype), default_tensor(2, dtype))
              ]
          },
          "_split_v2": {
              "ary,indices_or_sections": [
                  (default_tensor(2, dtype), (2, 3))
              ],
          },
          "slice": {
              "data,begin,end": [
                  (default_tensor(2, dtype), (0, 1), (2, 4))
              ],
          },
          "space_to_depth": {
              "data,block_size": [(default_tensor(4, dtype), 2)],
          },
          "_copy": {"data": [default_tensor(2, dtype)]},
          "_npi_transpose": {CFG_BASED_ON: "transpose"},
          "_npi_where": {CFG_BASED_ON: "where"},
          "_npx_reshape": {CFG_BASED_ON: "Reshape"},
          ###################################### Normalization ######################################
          "LayerNorm": {
              "data": [default_tensor(2, dtype)],
              "gamma": [TensorArg(common_weight_tensor)],
              "beta": [TensorArg(common_weight_tensor)],
          },
          "BatchNorm": {
              CFG_BASED_ON: "LayerNorm",
              "moving_mean": [TensorArg(common_weight_tensor)],
              "moving_var": [
                  TensorArg(
                      lambda shape, dtype: mx.nd.random.uniform(
                          0, 1, shape, dtype
                      )
                  )
              ],
          },
          "LRN": {"data,nsize": [(default_tensor(2, dtype), 3)]},
          ######################################## Reduction ########################################
          "mean": {"0": [default_tensor(2, dtype)], "axis": [0]},
          "sum": {CFG_BASED_ON: "mean"},
          "_npi_mean": {CFG_BASED_ON: "mean"},
          "_npi_sum": {CFG_BASED_ON: "mean"},
          ######################################### Softmax #########################################
          "softmax": {
              "data": [
                  default_tensor(2, dtype),
                  default_tensor(4, dtype),
              ],
              "axis": [-1],
          },
          "log_softmax": {CFG_BASED_ON: "softmax"},
          "masked_softmax": {
              CFG_BASED_ON: "softmax",
              "mask": [
                  CfgBasedArg(
                      lambda cfg: mx.nd.random.randint(
                          0, 2, cfg["data"].shape
                      ).astype("bool")
                  )
              ],
          },
          ################################### Activation / Unary ####################################
          "Activation": {
              "data": [default_tensor(2, dtype)],
              "act_type": [
                  "sigmoid",
                  "log_sigmoid",
                  "relu",
                  "softrelu",
                  "tanh",
                  "mish",
              ],
          },
          "LeakyReLU": {
              "data": [default_tensor(2, dtype)],
              "act_type": ["leaky", "elu", "gelu"],
          },
          "_npi_exp": {"0": [default_tensor(2, dtype)]},
          "_npi_sqrt": {
              "0": [
                  mx.nd.random.uniform(0, 8, DEFAULT_SHAPE * 2, dtype)
              ]
          },
          "_npi_square": {CFG_BASED_ON: "_npi_exp"},
          "_npi_tanh": {CFG_BASED_ON: "_npi_exp"},
          ######################################### Binary ##########################################
          "dot": {
              "0,1": [
                  (default_tensor(3, dtype), default_tensor(3, dtype))
              ],
          },
          "batch_dot": {CFG_BASED_ON: "dot"},
          "broadcast_add": {CFG_BASED_ON: "dot"},
          "broadcast_div": {CFG_BASED_ON: "dot"},
          "broadcast_mul": {CFG_BASED_ON: "dot"},
          "broadcast_sub": {CFG_BASED_ON: "dot"},
          "elemwise_add": {CFG_BASED_ON: "dot"},
          "_npi_dot": {CFG_BASED_ON: "dot"},
          "_npi_add": {CFG_BASED_ON: "dot"},
          "_npi_multiply": {CFG_BASED_ON: "dot"},
          "_npi_subtract": {CFG_BASED_ON: "dot"},
          "_npi_true_divide": {CFG_BASED_ON: "dot"},
          "add_n": {
              CFG_BASED_ON: "dot"
          },  # this is not binary, but can work as binary
          ######################################## Subgraph #########################################
          "_sg_onednn_conv": {
              CFG_BASED_ON: "Convolution",
              CFG_SUBGRAPH: [SubgraphCfg("Convolution", "ONEDNN")],
              "data,kernel": [
                  (default_tensor(4, dtype), (3, 3)),
                  (default_tensor(5, dtype), (3, 3, 3)),
              ],
          },
          "_sg_onednn_fully_connected": {
              CFG_BASED_ON: "FullyConnected",
              CFG_SUBGRAPH: [SubgraphCfg("FullyConnected", "ONEDNN")],
          },
          "_sg_onednn_batch_dot": {
              CFG_BASED_ON: "batch_dot",
              CFG_SUBGRAPH: [SubgraphCfg("batch_dot", "ONEDNN")],
          },
          "_sg_onednn_batch_norm": {CFG_BASED_ON: "BatchNorm"},
          "_sg_onednn_selfatt_qk": {
              CFG_SUBGRAPH: [
                  SubgraphCfg("_sg_onednn_selfatt_qk", "ONEDNN")
              ],
              "queries": [
                  mx.nd.random.normal(0, 1, (1, 4, 3 * 2 * 8), dtype)
              ],
              "keys": [
                  mx.nd.random.normal(0, 1, (1, 8, 3 * 2 * 8), dtype)
              ],
              "heads": [2],
          },
          "_sg_onednn_selfatt_qk_split": {
              CFG_SUBGRAPH: [
                  SubgraphCfg("_sg_onednn_selfatt_qk_split", "ONEDNN")
              ],
              "queries_keys_values": [
                  mx.nd.random.normal(0, 1, (1, 4, 3 * 2 * 8), dtype)
              ],
              "heads": [2],
          },
          "_sg_onednn_selfatt_valatt": {
              CFG_BASED_ON: "_sg_onednn_selfatt_qk_split",
              CFG_SUBGRAPH: [
                  SubgraphCfg("_sg_onednn_selfatt_valatt", "ONEDNN")
              ],
              "attention": [CfgBasedArg(valatt_attention_tensor)],
          },
      }
  
file_path: mxnet/tests/python/dnnl/op_cfg.py
imports: from collections import namedtuple
  from itertools import product
  from functools import partial
  import mxnet
  from mxnet.base import _is_np_op, _NP_OP_PREFIX, _NP_EXT_OP_PREFIX, _NP_INTERNAL_OP_PREFIX, _OP_NAME_PREFIX_LIST
label_desc: Use image node_id 5 for calling a global function with example usage: get_all_ops_cfgs(dtype) and returns: dict