class_docstr: Transforms the input vector to model_size with an one-layer MPL, i.e.,
  (batch_size, time_length, input_dim) -> (batch_size, time_length,
  model_size)
class_docstr_tok: ["Transforms","the","input","vector","to","model_size","with","an","one-layer","MPL",",","i.e.",",","(","batch_size",",","time_length",",","input_dim",")","-",">","(","batch_size",",","time_length",",","model_size",")"]
code: def __init__(self, model_size: int = 64, **kwargs) -> None:
      super().__init__(**kwargs)
  
      self.model_size = model_size
      with self.name_scope():
          self.net = mx.gluon.nn.Dense(
              units=self.model_size, flatten=False
          )
  
imports: from typing import Dict, Optional, Tuple
  import mxnet
  from mxnet.gluon import HybridBlock
  from gluonts.mx import Tensor
label_desc: Use image node_id 1 to create a new InputLayer object from inherited base classes: HybridBlock with example: obj = InputLayer(model_size)