docformatter

src/gluonts/torch/model/mq_cnn/estimator.py

@@ -456,7 +456,8 @@ def __init__(
  self.feat_static_real_dim = 0
 
  def create_transformation(self) -> Chain:
- """Creates transformation to be applied to input dataset
+ """
+ Creates transformation to be applied to input dataset.
 
  Returns:
  Chain:
@@ -577,7 +578,8 @@ def create_transformation(self) -> Chain:
  return Chain(transforms)
 
  def _create_instance_splitter(self, mode: str) -> Chain:
- """Creates instance splitter to be applied to the dataset
+ """
+ Creates instance splitter to be applied to the dataset.
 
  Args:
  mode (str): `training`, `validation` or `test`
@@ -676,7 +678,8 @@ def create_training_data_loader(
  shuffle_buffer_length: Optional[int] = None,
  **kwargs,
  ) -> Iterable:
- """Creates data loader for the training dataset
+ """
+ Creates data loader for the training dataset.
 
  Args:
  data (Dataset): training dataset
@@ -706,7 +709,8 @@ def create_validation_data_loader(
  module: MQCNNLightningModule,
  **kwargs,
  ) -> Iterable:
- """Creates data loader for the validation dataset
+ """
+ Creates data loader for the validation dataset.
 
  Args:
  data (Dataset): validation dataset
@@ -769,7 +773,8 @@ def create_predictor(
  transformation: Transformation,
  module: MQCNNLightningModule,
  ) -> PyTorchPredictor:
- """Creates predictor for inference
+ """
+ Creates predictor for inference.
 
  Args:
  transformation (Transformation): transformation to be applied to data input to predictor

src/gluonts/torch/model/mq_cnn/layers.py

@@ -253,8 +253,9 @@ def forward(
 
 class Enc2Dec(nn.Module):
  """
- Integrates the encoder_output_static, encoder_output_dynamic and future_features_dynamic
- and passes them through as the dynamic input to the decoder.
+ Integrates the encoder_output_static, encoder_output_dynamic and
+ future_features_dynamic and passes them through as the dynamic input to the
+ decoder.
 
  Parameters:
  ------------
@@ -439,7 +440,8 @@ def _get_local_mlp(self, init_dim, final_dim, hidden_dimension_seq):
  return local_mlp
 
  def forward(self, encoded_input: Tensor, future_input: Tensor) -> Tensor:
- """Forward pass for MQCNN decoder
+ """
+ Forward pass for MQCNN decoder.
 
  Args:
  encoded_input (Tensor):

src/gluonts/transform/split.py

@@ -578,7 +578,9 @@ def flatmap_transform(
 
 
 class ForkingSequenceSplitter(FlatMapTransformation):
- """Forking sequence splitter used by MQ-CNN Model"""
+ """
+ Forking sequence splitter used by MQ-CNN Model.
+ """
 
  @validated()
  def __init__(
@@ -597,7 +599,8 @@ def __init__(
  start_input_field: str = FieldName.TARGET,
  lead_time: int = 0,
  ) -> None:
- """Creates forking sequences
+ """
+ Creates forking sequences.
 
  Args:
  instance_sampler ([type]):