code: def forward(
      self,
      inputs: Union[
          Dict[str, torch.Tensor],
          Dict[str, np.ndarray],
          Tuple[Dict[str, torch.Tensor], Dict[str, torch.Tensor]],
      ],
      mask=None,
  ) -> Dict[str, torch.Tensor]:
      # Invoke output features.
      output_logits = {}
      output_feature_name = self.output_features.keys()[0]
      output_feature = self.output_features.get(output_feature_name)
  
      # If `inputs` is a tuple, it should contain `(inputs, targets)`.
      if isinstance(inputs, tuple):
          inputs, _ = inputs
  
      assert list(inputs.keys()) == self.input_features.keys()
  
      # If the model has not been compiled, predict using the LightGBM sklearn iterface. Otherwise, use torch with
      # the Hummingbird compiled model. Notably, when compiling the model to torchscript, compiling with Hummingbird
      # first should preserve the torch predictions code path.
      if self.compiled_model is None:
          feature_vectors = []
          for a in inputs.values():
              if a.ndim > 1:
                  # Input feature is a vector of shape [batch_size, nfeatures]
                  # We need to expand this into `nfeatures` individual vectors of shape [batch_size]
                  nfeatures = a.shape[1]
                  vectors = [
                      v.squeeze() for v in np.hsplit(a, nfeatures)
                  ]
                  feature_vectors += vectors
              else:
                  feature_vectors.append(a)
  
          # The LGBM sklearn interface works with array-likes, so we place the inputs into a 2D numpy array.
          in_array = np.stack(feature_vectors, axis=0).T
  
          # Predict on the input batch and convert the predictions to torch tensors so that they are compatible with
          # the existing metrics modules.
          # Input: 2D eval_batch_size x n_features array
          # Output: 1D eval_batch_size array if regression, else 2D eval_batch_size x n_classes array
          logits = torch.from_numpy(
              self.lgbm_model.predict(in_array, raw_score=True)
          )
          logits = reshape_logits(output_feature, logits)
      else:
          # Convert inputs to tensors of type float as expected by hummingbird GEMMTreeImpl.
          for input_feature_name, t in inputs.items():
              if not isinstance(t, torch.Tensor):
                  inputs[input_feature_name] = torch.from_numpy(
                      t
                  ).float()
              else:
                  # For scalar features, expect tensor of [batch_size, 1] for input, so reshape if only
                  # given [batch_size]. Additionally, because TorchScript tracing will not retain this conditional,
                  # we need to make sure we perform this reshaping for every scalar feature, even if it's already
                  # shaped correctly at the time of tracing, to allow for input in either shape at inference time.
                  if len(t.shape) == 1 or (
                      len(t.shape) == 2 and t.shape[1] == 1
                  ):
                      t = t.view(-1, 1)
                  inputs[input_feature_name] = t.float()
  
          # TODO(travis): include encoder and decoder steps during inference
          # encoder_outputs = {}
          # for input_feature_name, input_values in inputs.items():
          #     encoder = self.input_features.get(input_feature_name)
          #     encoder_output = encoder(input_values)
          #     encoder_outputs[input_feature_name] = encoder_output
  
          # concatenate inputs
          inputs = torch.cat(list(inputs.values()), dim=1)
  
          assert (
              isinstance(inputs, torch.Tensor)
              and inputs.dtype == torch.float32
              and inputs.ndim == 2
          ), (
              f"Expected inputs to be a 2D tensor of shape (batch_size, n) of type float32, "
              f"but got shape {inputs.shape} and type {inputs.dtype}"
          )
          # Predict using PyTorch module, so it is included when converting to TorchScript.
          preds = self.compiled_model(inputs)
  
          if output_feature.type() == NUMBER:
              # regression
              logits = preds.view(-1)
          else:
              # classification
              _, logits = preds
              logits = reshape_logits(output_feature, logits)
  
              if output_feature.type() == BINARY:
                  logits = logits.view(-1)
  
      output_feature_utils.set_output_feature_tensor(
          output_logits, output_feature_name, LOGITS, logits
      )
  
      # 1 token for each input feature + 1 token for the output feature.
      output_logits[USED_TOKENS] = get_used_tokens_for_gbm(inputs)
      return output_logits
  
imports: import os
  from contextlib import contextmanager
  from typing import Dict, Optional, Tuple, Union
  import lightgbm
  import numpy
  import torch
  from hummingbird.ml import convert
  from ludwig.accounting.used_tokens import get_used_tokens_for_gbm
  from ludwig.constants import BINARY, LOGITS, MODEL_GBM, NUMBER, USED_TOKENS
  from ludwig.features.base_feature import OutputFeature
  from ludwig.globals import MODEL_WEIGHTS_FILE_NAME
  from ludwig.models.base import BaseModel
  from ludwig.schema.features.base import BaseOutputFeatureConfig, FeatureCollection
  from ludwig.schema.model_config import ModelConfig
  from ludwig.utils import output_feature_utils
  from ludwig.utils.fs_utils import path_exists
  from ludwig.utils.gbm_utils import reshape_logits
  from ludwig.utils.torch_utils import get_torch_device
  from ludwig.utils.types import TorchDevice
label_desc: Use image node_id 5 for calling the GBM obj's underlying member method code with example usage: obj.forward(inputs, mask) and returns: output_logits