docstr: Preprocess the input matrix
  :param input tensor
code: def forward(
      self, input: torch.Tensor, input_presence_byte: torch.Tensor
  ) -> torch.Tensor:
      """Preprocess the input matrix
      :param input tensor
      """
      assert (
          input.shape
          == input_presence_byte.shape
          # pyre-fixme[16]: Callable `input` has no attribute `shape`.
      ), f"{input.shape} != {input_presence_byte.shape}"
      outputs = []
      split_input = torch.split(input, self.split_sections, dim=1)
      # NB: converting to float prevent ASAN heap-buffer-overflow
      split_presence = torch.split(
          input_presence_byte.float(), self.split_sections, dim=1
      )
      ptr = 0
      for i, feature_type in enumerate(FEATURE_TYPES):
          begin_index = self.feature_starts[i]
          if (i + 1) == len(FEATURE_TYPES):
              end_index = len(self.normalization_parameters)
          else:
              end_index = self.feature_starts[i + 1]
          if begin_index == end_index:
              continue  # No features of this type
          if feature_type == ENUM:
              # Process one-at-a-time
              for j in range(begin_index, end_index):
                  norm_params = self.normalization_parameters[
                      self.sorted_features[j]
                  ]
                  new_output = (
                      self._preprocess_feature_single_column(
                          j, split_input[ptr], norm_params
                      )
                      * split_presence[ptr]
                  )
                  ptr += 1
                  self._check_preprocessing_output(
                      new_output, [norm_params]
                  )
                  outputs.append(new_output)
          else:
              norm_params_list: List[NormalizationParameters] = []
              for f in self.sorted_features[begin_index:end_index]:
                  norm_params_list.append(
                      self.normalization_parameters[f]
                  )
              new_output = (
                  self._preprocess_feature_multi_column(
                      begin_index, split_input[ptr], norm_params_list
                  )
                  * split_presence[ptr]
              )
              ptr += 1
              self._check_preprocessing_output(
                  new_output, norm_params_list
              )
              if feature_type != DO_NOT_PREPROCESS:
                  new_output = torch.clamp(
                      new_output, MIN_FEATURE_VALUE, MAX_FEATURE_VALUE
                  )
              outputs.append(new_output)
  
      return torch.cat(outputs, dim=1)
  
imports: import logging
  from typing import cast, Dict, List, Optional, Tuple
  import torch
  from reagent.core.parameters import NormalizationParameters
  from reagent.preprocessing.identify_types import DO_NOT_PREPROCESS, ENUM, FEATURE_TYPES
  from reagent.preprocessing.normalization import EPS, MAX_FEATURE_VALUE, MIN_FEATURE_VALUE
  from torch.nn import Module, Parameter
label_desc: Use image node_id 3 for calling the Preprocessor obj's underlying member method code with example usage: obj.forward(input, input_presence_byte) and returns: torch