class_docstr: 1-dimensional transposed convolutional layer, also known as deconvolution layer
  (specification key: `deconv1d`).
  
  Args:
      size (int >= 0): Layer output size, 0 implies additionally removing the axis
          (<span style="color:#C00000"><b>required</b></span>).
      window (int > 0): Window size
          (<span style="color:#00C000"><b>default</b></span>: 3).
      output_width (int > 0): Output width
          (<span style="color:#00C000"><b>default</b></span>: same as input).
      stride (int > 0): Stride size
          (<span style="color:#00C000"><b>default</b></span>: 1).
      padding ('same' | 'valid'): Padding type, see
          `TensorFlow docs <https://www.tensorflow.org/api_docs/python/tf/nn/convolution>`__
          (<span style="color:#00C000"><b>default</b></span>: 'same').
      dilation (int > 0 | (int > 0, int > 0)): Dilation value
          (<span style="color:#00C000"><b>default</b></span>: 1).
      bias (bool): Whether to add a trainable bias variable
          (<span style="color:#00C000"><b>default</b></span>: true).
      activation ('crelu' | 'elu' | 'leaky-relu' | 'none' | 'relu' | 'selu' | 'sigmoid' |
          'softmax' | 'softplus' | 'softsign' | 'swish' | 'tanh'): Activation nonlinearity
          (<span style="color:#00C000"><b>default</b></span>: "relu").
      dropout (parameter, 0.0 <= float < 1.0): Dropout rate
          (<span style="color:#00C000"><b>default</b></span>: 0.0).
      initialization_scale (float > 0.0): Initialization scale
          (<span style="color:#00C000"><b>default</b></span>: 1.0).
      vars_trainable (bool): Whether layer variables are trainable
          (<span style="color:#00C000"><b>default</b></span>: true).
      l2_regularization (float >= 0.0): Scalar controlling L2 regularization
          (<span style="color:#00C000"><b>default</b></span>: inherit value of parent module).
      name (string): Layer name
          (<span style="color:#00C000"><b>default</b></span>: internally chosen).
      input_spec (specification): <span style="color:#00C000"><b>internal use</b></span>.
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code: def __init__(
      self,
      *,
      size,
      window=3,
      output_width=None,
      stride=1,
      padding="same",
      dilation=1,
      bias=True,
      activation="relu",
      dropout=0.0,
      initialization_scale=1.0,
      vars_trainable=True,
      l2_regularization=None,
      name=None,
      input_spec=None,
  ):
      self.window = window
      if output_width is None:
          self.output_shape = None
      elif self.squeeze:
          self.output_shape = (output_width, max(1, size))
      self.stride = stride
      self.padding = padding
      self.dilation = dilation
  
      super().__init__(
          name=name,
          size=size,
          bias=bias,
          activation=activation,
          dropout=dropout,
          vars_trainable=vars_trainable,
          input_spec=input_spec,
          l2_regularization=l2_regularization,
      )
  
      self.initialization_scale = initialization_scale
  
      self.architecture_kwargs["size"] = str(size)
      self.architecture_kwargs["window"] = str(window)
      if output_width is not None:
          self.architecture_kwargs["output_width"] = str(output_width)
      self.architecture_kwargs["padding"] = str(padding)
      if stride != 1:
          self.architecture_kwargs["stride"] = str(stride)
      if dilation != 1:
          self.architecture_kwargs["dilation"] = str(dilation)
      self.architecture_kwargs["bias"] = str(bias)
      if activation is not None:
          self.architecture_kwargs["activation"] = str(activation)
      if dropout != 0.0:
          self.architecture_kwargs["dropout"] = str(dropout)
      if initialization_scale != 1.0:
          self.architecture_kwargs["initialization_scale"] = str(
              initialization_scale
          )
      if not vars_trainable:
          self.architecture_kwargs["trainable"] = str(vars_trainable)
      if l2_regularization is not None:
          self.architecture_kwargs["l2_regularization"] = str(
              l2_regularization
          )
  
imports: import tensorflow
  from tensorflow.python.keras.utils.conv_utils import conv_output_length, deconv_output_length
  from tensorforce import TensorforceError, util
  from tensorforce.core import TensorSpec, tf_function, tf_util
  from tensorforce.core.layers import TransformationBase
label_desc: Use image node_id 1 to create a new Conv1dTranspose object from inherited base classes: TransformationBase with example: obj = Conv1dTranspose()