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 _predict_common(self, output_index, *inputs):
      # Compute padding.
      padding_size = 0
      if self._pad_input:
          padding_size = self._batch_size - inputs[0].shape[0]
  
      # Prepare inputs.
      for i, input_ in enumerate(inputs):
          assert (
              self._pad_input or input_.shape[0] == self._batch_size
          ), "The number of input rows {} is different from the batch size {} the TVM model is compiled for.".format(
              input_.shape[0], self._batch_size
          )
          if padding_size > 0:
              input_ = np.pad(input_, [(0, padding_size), (0, 0)])
          self._tvm_tensors[self._input_names[i]] = self._to_tvm_array(
              input_
          )
  
      # Compute the predictions.
      self.model.run(**self._tvm_tensors)
      result = self.model.get_output(output_index).asnumpy()
  
      # Remove padding if necessary.
      if padding_size > 0:
          result = result[:-padding_size]
      return result
  
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 4 for calling the TVMSklearnContainer obj's underlying member method code with example usage: obj._predict_common(output_index) and returns: result