docstr: Prepares the inputs for the network by repeating static feature and
  concatenating it with time features and observed value indicator.
  
  Parameters
  ----------
  F
  feat_static_cat
      Static categorical features: (batch_size, num_cat_features)
  past_observed_values
      Observed value indicator for the past target: (batch_size,
      receptive_field)
  past_time_feat
      Past time features: (batch_size, num_time_features,
      receptive_field)
  future_time_feat
      Future time features: (batch_size, num_time_features, pred_length)
  future_observed_values
      Observed value indicator for the future target:
      (batch_size, pred_length). This will be set to all ones, if not
      provided (e.g., during prediction).
  scale
      scale of the time series: (batch_size, 1)
  Returns
  -------
  Tensor
      A tensor containing all the features ready to be passed through the
      network. Shape:
      (batch_size, num_features, receptive_field + pred_length)
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code: def get_full_features(
      self,
      F,
      feat_static_cat: Tensor,
      past_observed_values: Tensor,
      past_time_feat: Tensor,
      future_time_feat: Tensor,
      future_observed_values: Optional[Tensor],
      scale: Tensor,
  ):
      """
      Prepares the inputs for the network by repeating static feature and
      concatenating it with time features and observed value indicator.
  
      Parameters
      ----------
      F
      feat_static_cat
          Static categorical features: (batch_size, num_cat_features)
      past_observed_values
          Observed value indicator for the past target: (batch_size,
          receptive_field)
      past_time_feat
          Past time features: (batch_size, num_time_features,
          receptive_field)
      future_time_feat
          Future time features: (batch_size, num_time_features, pred_length)
      future_observed_values
          Observed value indicator for the future target:
          (batch_size, pred_length). This will be set to all ones, if not
          provided (e.g., during prediction).
      scale
          scale of the time series: (batch_size, 1)
      Returns
      -------
      Tensor
          A tensor containing all the features ready to be passed through the
          network. Shape:
          (batch_size, num_features, receptive_field + pred_length)
      """
      embedded_cat = self.feature_embedder(feat_static_cat)
      static_feat = F.concat(embedded_cat, F.log(scale + 1.0), dim=1)
      repeated_static_feat = F.repeat(
          F.expand_dims(static_feat, axis=-1),
          repeats=self.pred_length + self.receptive_field,
          axis=-1,
      )
  
      if future_observed_values is None:
          future_observed_values = (
              F.slice_axis(future_time_feat, begin=0, end=1, axis=1)
              .squeeze(axis=1)
              .ones_like()
          )
      full_observed = F.expand_dims(
          F.concat(
              past_observed_values, future_observed_values, dim=-1
          ),
          axis=1,
      )
      full_time_features = F.concat(
          past_time_feat, future_time_feat, dim=-1
      )
      full_features = F.concat(
          full_time_features, full_observed, repeated_static_feat, dim=1
      )
      return full_features
  
imports: import math
  from typing import List, Optional
  import mxnet
  from mxnet import gluon
  from mxnet.gluon import nn
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
  from gluonts.mx.block.feature import FeatureEmbedder
label_desc: Use image node_id 5 for calling the WaveNet obj's underlying member method code with example usage: obj.get_full_features(F, feat_static_cat, past_observed_values, past_time_feat, future_time_feat, future_observed_values, scale) and returns: full_features