class_docstr: The ``TabularRegressor`` is a :class:`~flash.Task` for classifying tabular data. For more details, see
  :ref:`tabular_classification`.
  
  Args:
      parameters: The parameters computed from the training data (can be obtained from the ``parameters`` attribute of
          the ``TabularRegressionData`` object containing your training data).
      embedding_sizes: List of (num_classes, emb_dim) to form categorical embeddings.
      cat_dims: Number of distinct values for each categorical column.
      num_features: Number of columns in table.
      backbone: name of the model to use.
      loss_fn: Loss function for training, defaults to mean squared error.
      optimizer: Optimizer to use for training.
      lr_scheduler: The LR scheduler to use during training.
      metrics: Metrics to compute for training and evaluation. Can either be an metric from the `torchmetrics`
          package, a custom metric inherenting from `torchmetrics.Metric`, a callable function or a list/dict
          containing a combination of the aforementioned. In all cases, each metric needs to have the signature
          `metric(preds,target)` and return a single scalar tensor. Defaults to :class:`torchmetrics.Accuracy`.
      learning_rate: Learning rate to use for training.
      **backbone_kwargs: Optional additional arguments for the model.
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docstr: Get the parameters computed from the training data used to create this
  :class:`~flash.tabular.regression.TabularRegressor`. Use these parameters to load data for evaluation /
  prediction.
  
  Example::
  
      import flash
      from flash.core.data.utils import download_data
      from flash.tabular import TabularRegressionData, TabularRegressor
      download_data("https://pl-flash-data.s3.amazonaws.com/SeoulBikeData.csv", "./data")
      model = TabularRegressor.load_from_checkpoint(
          "https://flash-weights.s3.amazonaws.com/0.7.0/tabular_regression_model.pt"
      )
      datamodule = TabularRegressionData.from_csv(
          predict_file="data/SeoulBikeData.csv",
          parameters=model.data_parameters,
          batch_size=8,
      )
      trainer = flash.Trainer()
      trainer.predict(
          model,
          datamodule=datamodule,
      )
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code: def data_parameters(self) -> Dict[str, Any]:
      r"""Get the parameters computed from the training data used to create this
      :class:`~flash.tabular.regression.TabularRegressor`. Use these parameters to load data for evaluation /
      prediction.
  
      Example::
  
          import flash
          from flash.core.data.utils import download_data
          from flash.tabular import TabularRegressionData, TabularRegressor
          download_data("https://pl-flash-data.s3.amazonaws.com/SeoulBikeData.csv", "./data")
          model = TabularRegressor.load_from_checkpoint(
              "https://flash-weights.s3.amazonaws.com/0.7.0/tabular_regression_model.pt"
          )
          datamodule = TabularRegressionData.from_csv(
              predict_file="data/SeoulBikeData.csv",
              parameters=model.data_parameters,
              batch_size=8,
          )
          trainer = flash.Trainer()
          trainer.predict(
              model,
              datamodule=datamodule,
          )
  
      """
      return self._data_parameters
  
file_path: lightning-flash/src/flash/tabular/regression/model.py
imports: from functools import partial
  from typing import Any, Callable, Dict, Optional, Type, Union
  from torch.nn import functional
  from flash.core.data.io.input import ServeInput
  from flash.core.data.io.input_transform import InputTransform
  from flash.core.data.io.output import Output
  from flash.core.integrations.pytorch_tabular.backbones import PYTORCH_TABULAR_BACKBONES
  from flash.core.registry import FlashRegistry
  from flash.core.regression import RegressionAdapterTask
  from flash.core.serve import Composition
  from flash.core.utilities.imports import _TOPIC_TABULAR_AVAILABLE, requires
  from flash.core.utilities.types import INPUT_TRANSFORM_TYPE, LR_SCHEDULER_TYPE, METRICS_TYPE, OPTIMIZER_TYPE
  from flash.tabular.input import TabularDeserializer
label_desc: Use image node_id 2 for calling the TabularRegressor obj's underlying member method code with example usage: obj.data_parameters() and returns: self