class_docstr: Base Model that can be used for all array-based
  ApacheTVMInferenceLearners.
class_docstr_tok: ["Base","Model","that","can","be","used","for","all","array-based","ApacheTVMInferenceLearners","."]
code: def _inner_predict(
      self,
      input_arrays: Generator[np.ndarray, None, None],
      input_shapes: Optional[List[Tuple[int, ...]]],
  ) -> Generator[np.ndarray, None, None]:
      if self.network_parameters.dynamic_info is not None:
          input_arrays = (
              np.pad(
                  input_array,
                  [
                      (0, abs(x - y))
                      for x, y in zip(
                          input_array.shape,
                          input_size,
                      )
                  ],
                  mode="constant",
                  constant_values=0,
              )
              for input_array, input_size in zip(
                  input_arrays, self.network_parameters.input_sizes
              )
          )
  
      output_arrays = self._predict_array(input_arrays)
      if self.network_parameters.dynamic_info is not None:
          assert input_shapes is not None
          dynamic_info = self.network_parameters.dynamic_info
          return (
              output_array[
                  tuple(
                      slice(
                          0,
                          None
                          if x not in out_dynamic_dict.keys()
                          else dynamic_info.retrieve_output_dim(
                              input_shapes, j, i, x
                          ),
                      )
                      for i, x in enumerate(output_array.shape)
                  )
              ]
              for j, (output_array, out_dynamic_dict) in enumerate(
                  zip(output_arrays, dynamic_info.outputs)
              )
          )
  
      return output_arrays
  
imports: import os
  import shutil
  from abc import ABC
  from pathlib import Path
  from tempfile import TemporaryDirectory
  from typing import Union, Type, Dict, Any, List, Generator, Tuple, Optional
  import numpy
  from nebullvm.config import TVM_FILENAMES
  from nebullvm.core.models import Device, ModelParams, DeepLearningFramework
  from nebullvm.operations.inference_learners.base import BaseInferenceLearner, LearnerMetadata, PytorchBaseInferenceLearner, TensorflowBaseInferenceLearner, NumpyBaseInferenceLearner
  from nebullvm.optional_modules.tensorflow import tensorflow
  from nebullvm.optional_modules.torch import torch
  from nebullvm.optional_modules.tvm import GraphModule, tvm, ExecutorFactoryModule
  from nebullvm.tools.data import DataManager
  from nebullvm.tools.transformations import MultiStageTransformation, HalfPrecisionTransformation
label_desc: Use image node_id 1 for calling the BaseArrayApacheTVMInferenceLearner obj's underlying member method code with example usage: obj._inner_predict(input_arrays, input_shapes) and returns: output_arrays, unknown