class_docstr: Constructs an LSTNet estimator for multivariate time series data.
  
  The model has been described in this paper:
  https://arxiv.org/abs/1703.07015
  
  Note that this implementation will change over time as we further work on
  this method.
  
  Parameters
  ----------
  prediction_length
      Length of the prediction p where given `(y_1, ..., y_t)` the model
      predicts `(y_{t+l+1}, ..., y_{t+l+p})`, where l is `lead_time`
  context_length
      The maximum number of steps to unroll the RNN for computing the
      predictions
      (Note that it is constraints by the Conv2D output size)
  num_series
      Number of time series (covariates)
  skip_size
      Skip size for the skip RNN layer
  ar_window
      Auto-regressive window size for the linear part
  channels
      Number of channels for first layer Conv2D
  lead_time
      Lead time (default: 0)
  kernel_size
      Kernel size for first layer Conv2D (default: 6)
  trainer
      Trainer object to be used (default: Trainer())
  dropout_rate
      Dropout regularization parameter (default: 0.2)
  output_activation
      The last activation to be used for output.
      Accepts either `None` (default no activation), `sigmoid` or `tanh`
  rnn_cell_type
      Type of the RNN cell. Either `lstm` or `gru` (default: `gru`)
  rnn_num_layers
      Number of RNN layers to be used
  rnn_num_cells
      Number of RNN cells for each layer (default: 100)
  skip_rnn_cell_type
      Type of the RNN cell for the skip layer. Either `lstm` or `gru` (
      default: `gru`)
  skip_rnn_num_layers
      Number of RNN layers to be used for skip part
  skip_rnn_num_cells
      Number of RNN cells for each layer for skip part (default: 10)
  scaling
      Whether to automatically scale the target values (default: True)
  train_sampler
      Controls the sampling of windows during training.
  validation_sampler
      Controls the sampling of windows during validation.
  batch_size
      The size of the batches to be used training and prediction.
  dtype
      Data type (default: np.float32)
code: def create_transformation(self) -> Transformation:
      return AsNumpyArray(
          field=FieldName.TARGET, expected_ndim=2, dtype=self.dtype
      ) + AddObservedValuesIndicator(
          target_field=FieldName.TARGET,
          output_field=FieldName.OBSERVED_VALUES,
          dtype=self.dtype,
      )
  
imports: from functools import partial
  from typing import Optional, Type
  import numpy
  from mxnet.gluon import HybridBlock
  from gluonts.core.component import validated
  from gluonts.dataset.common import Dataset
  from gluonts.dataset.field_names import FieldName
  from gluonts.dataset.loader import DataLoader, TrainDataLoader, ValidationDataLoader
  from gluonts.mx.model.lstnet._network import LSTNetPredict, LSTNetTrain
  from gluonts.model.predictor import Predictor
  from gluonts.mx.batchify import batchify
  from gluonts.mx.model.estimator import GluonEstimator
  from gluonts.mx.model.predictor import RepresentableBlockPredictor
  from gluonts.mx.trainer import Trainer
  from gluonts.mx.util import copy_parameters, get_hybrid_forward_input_names
  from gluonts.transform import AddObservedValuesIndicator, AsNumpyArray, ExpectedNumInstanceSampler, InstanceSampler, InstanceSplitter, SelectFields, TestSplitSampler, Transformation, ValidationSplitSampler
label_desc: Use image node_id 2 for calling the LSTNetEstimator obj's underlying member method code with example usage: obj.create_transformation() and returns: unknown