class_docstr: Base container for TVM models.
  The container allows to mirror the Sklearn API.
  The test input size must be the same as the batch size this container is created.
code: def __init__(
      self, model, n_threads=None, batch_size=None, extra_config={}
  ):
      super(TVMSklearnContainer, self).__init__(
          model, n_threads, batch_size, extra_config=extra_config
      )
  
      assert tvm_installed(), "TVM Container requires TVM installed."
  
      self._ctx = self._extra_config[constants.TVM_CONTEXT]
      self._to_tvm_array = lambda x: tvm.nd.array(x, self._ctx)
      self._input_names = self._extra_config[constants.TVM_INPUT_NAMES]
      self._tvm_tensors = {
          name: self._to_tvm_array(np.array([]))
          for name in self._input_names
      }
      self._pad_input = (
          self._extra_config[constants.TVM_PAD_INPUT]
          if constants.TVM_PAD_INPUT in self._extra_config
          else False
      )
  
      os.environ["TVM_NUM_THREADS"] = str(self._n_threads)
  
imports: import dill
  import os
  import numpy
  import shutil
  import torch
  import warnings
  import hummingbird
  from hummingbird.ml._utils import tvm_installed, dump_versions, check_dumped_versions
  from hummingbird.ml.operator_converters import constants
  from hummingbird.ml.containers._sklearn_api_containers import SklearnContainer, SklearnContainerTransformer, SklearnContainerRegression, SklearnContainerClassification, SklearnContainerAnomalyDetection
label_desc: Use image node_id 1 to create a new TVMSklearnContainer object from inherited base classes: SklearnContainer with example: obj = TVMSklearnContainer(model, n_threads, batch_size, extra_config)