class_docstr: The :class:`Input` class is the starting layer of a neural network.
  
  Parameters
  ----------
  shape : tuple (int)
      Including batch size.
  dtype: dtype
      The type of input values. By default, tf.float32.
  name : None or str
      A unique layer name.
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code: def __init__(self, shape, dtype=tf.float32, name=None):  #'input'):
      # super(InputLayer, self).__init__(prev_layer=inputs, name=name)
      super(_InputLayer, self).__init__(name)
  
      if isinstance(dtype, str):
          try:
              dtype = eval(dtype)
          except Exception as e:
              raise RuntimeError(
                  "%s is not a valid dtype for InputLayer." % (dtype)
              )
      if not isinstance(dtype, tf.DType):
          raise RuntimeError(
              "%s is not a valid dtype for InputLayer." % (dtype)
          )
  
      logging.info("Input  %s: %s" % (self.name, str(shape)))
      self.shape = shape  # shape is needed in __repr__
  
      shape_without_none = [_ if _ is not None else 1 for _ in shape]
      # self.outputs = self.forward(tl.initializers.random_normal()(shape_without_none))
      outputs = self.forward(
          tl.initializers.ones()(shape_without_none, dtype=dtype)
      )
  
      self._built = True
  
      self._add_node(outputs, outputs)
  
file_path: TensorLayer/tensorlayer/layers/inputs.py
imports: import numpy
  import tensorflow
  import tensorlayer
  from tensorlayer import logging
  from tensorlayer.layers.core import Layer, LayerNode
label_desc: Use image node_id 1 to create a new _InputLayer object from inherited base classes: Layer with example: obj = _InputLayer(shape, dtype, name)