class_docstr: Layer that reshapes inputs into the given shape.
  
  Args:
      target_shape: Target shape. Tuple of integers, does not include the
          samples dimension (batch size).
  
  Input shape:
      Arbitrary, although all dimensions in the input shape must be
      known/fixed. Use the keyword argument `input_shape` (tuple of integers,
      does not include the samples/batch size axis) when using this layer as
      the first layer in a model.
  
  Output shape:
      `(batch_size, *target_shape)`
  
  Example:
  
  >>> x = keras.Input(shape=(12,))
  >>> y = keras.layers.Reshape((3, 4))(x)
  >>> y.shape
  (None, 3, 4)
  
  >>> # also supports shape inference using `-1` as dimension
  >>> y = keras.layers.Reshape((-1, 2, 2))(x)
  >>> y.shape
  (None, 3, 2, 2)
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code: def compute_output_shape(self, input_shape):
      return (
          input_shape[0],
          *operation_utils.compute_reshape_output_shape(
              input_shape[1:], self.target_shape, "target_shape"
          ),
      )
  
file_path: keras/keras/layers/reshaping/reshape.py
imports: from keras import ops
  from keras.api_export import keras_export
  from keras.backend.common.keras_tensor import KerasTensor
  from keras.layers.layer import Layer
  from keras.ops import operation_utils
label_desc: Use image node_id 2 for calling the Reshape obj's underlying member method code with example usage: obj.compute_output_shape(input_shape) and returns: input_shape,  unknown