class_docstr: NASNet Cell class that is used as a 'layer' in image architectures.
class_docstr_tok: ["NASNet","Cell","class","that","is","used","as","a","'layer","'","in","image","architectures","."]
docstr: Constructs a NasNetABaseCell.
  
  Args:
    num_conv_filters: The number of filters for each convolution operation.
    operations: List of operations that are performed in the NASNet Cell in
      order.
    used_hiddenstates: Binary array that signals if the hiddenstate was used
      within the cell. This is used to determine what outputs of the cell
      should be concatenated together.
    hiddenstate_indices: Determines what hiddenstates should be combined
      together with the specified operations to create the NASNet cell.
    drop_path_keep_prob: Keep probability during DropPath regularization.
    total_num_cells: Total number of cells.
    total_training_steps: Total training steps.
    use_bounded_activation: Whether or not to use bounded activations. Bounded
      activations better lend themselves to quantized inference.
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code: def __init__(
      self,
      num_conv_filters,
      operations,
      used_hiddenstates,
      hiddenstate_indices,
      drop_path_keep_prob,
      total_num_cells,
      total_training_steps,
      use_bounded_activation=False,
  ):
      """Constructs a NasNetABaseCell.
  
      Args:
        num_conv_filters: The number of filters for each convolution operation.
        operations: List of operations that are performed in the NASNet Cell in
          order.
        used_hiddenstates: Binary array that signals if the hiddenstate was used
          within the cell. This is used to determine what outputs of the cell
          should be concatenated together.
        hiddenstate_indices: Determines what hiddenstates should be combined
          together with the specified operations to create the NASNet cell.
        drop_path_keep_prob: Keep probability during DropPath regularization.
        total_num_cells: Total number of cells.
        total_training_steps: Total training steps.
        use_bounded_activation: Whether or not to use bounded activations. Bounded
          activations better lend themselves to quantized inference.
      """
      self._num_conv_filters = num_conv_filters
      self._operations = operations
      self._used_hiddenstates = used_hiddenstates
      self._hiddenstate_indices = hiddenstate_indices
      self._drop_path_keep_prob = drop_path_keep_prob
      self._total_num_cells = total_num_cells
      self._total_training_steps = total_training_steps
      self._use_bounded_activation = use_bounded_activation
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow.compat.v1
label_desc: Use image node_id 1 to create a new NasNetABaseCell object from inherited base classes: object with example: obj = NasNetABaseCell(num_conv_filters, operations, used_hiddenstates, hiddenstate_indices, drop_path_keep_prob, total_num_cells, total_training_steps, use_bounded_activation)