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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def _infer_all_output_dims(self, inputs):
 """Calculate the output shape for `inputs` after a deconvolution. Args: inputs: A Tensor of shape `data_format` and of type `tf.float16`, `tf.bfloat16` or `tf.float32`. Returns: output_shape: A tensor of shape (`batch_size`, `conv_output_shape`). """

    # Use tensorflow shape op to manipulate inputs shape, so that unknown batch
    # size - which can happen when using input placeholders - is handled
    # correcly.
    batch_size = tf.expand_dims(tf.shape(inputs)[0], 0)
    out_channels = (self.output_channels,)

    # Height dim needs to be added to everything for 1D Conv
    # as we'll be using the 2D Conv Transpose op.
    if self._n == 1:
      out_shape = (1,) + self.output_shape
    else:
      out_shape = self.output_shape

    if self._data_format.startswith("NC"):
      out_shape_tuple = out_channels + out_shape
    elif self._data_format.startswith("N") and self._data_format.endswith("C"):
      out_shape_tuple = out_shape + out_channels

    output_shape = tf.concat([batch_size, out_shape_tuple], 0)
    return output_shape