code: def strided_app_2d(
      matrix: np.ndarray, batch_size: int, step: int
  ) -> np.ndarray:
      matrix_length = matrix.shape[0]
      matrix_width = matrix.shape[1]
      if batch_size > matrix_length:
          return np.expand_dims(matrix, axis=0)
      batch_count = ((matrix_length - batch_size) // step) + 1
      stride_length, stride_width = matrix.strides
      return np.lib.stride_tricks.as_strided(
          matrix,
          shape=(batch_count, batch_size, matrix_width),
          strides=(step * stride_length, stride_length, stride_width),
      )
  
imports: import numpy
  import statsmodels.api
label_desc: Use image node_id 20 for calling a global function with example usage: strided_app_2d(matrix, batch_size, step) and returns: np, np