def reshape(self, *shape, **kwargs):
        """Returns a **view** of this array with a new shape without altering any data.

        Parameters
        ----------
        shape : tuple of int, or n ints
            The new shape should not change the array size, namely
            ``np.prod(new_shape)`` should be equal to ``np.prod(self.shape)``.
            Some dimensions of the shape can take special values from the set {0, -1, -2, -3, -4}.
            The significance of each is explained below:

            - ``0``  copy this dimension from the input to the output shape.

              Example::

              - input shape = (2,3,4), shape = (4,0,2), output shape = (4,3,2)
              - input shape = (2,3,4), shape = (2,0,0), output shape = (2,3,4)

            - ``-1`` infers the dimension of the output shape by using the remainder of the
              input dimensions keeping the size of the new array same as that of the input array.
              At most one dimension of shape can be -1.

              Example::

              - input shape = (2,3,4), shape = (6,1,-1), output shape = (6,1,4)
              - input shape = (2,3,4), shape = (3,-1,8), output shape = (3,1,8)
              - input shape = (2,3,4), shape=(-1,), output shape = (24,)

            - ``-2`` copy all/remainder of the input dimensions to the output shape.

              Example::

              - input shape = (2,3,4), shape = (-2,), output shape = (2,3,4)
              - input shape = (2,3,4), shape = (2,-2), output shape = (2,3,4)
              - input shape = (2,3,4), shape = (-2,1,1), output shape = (2,3,4,1,1)

            - ``-3`` use the product of two consecutive dimensions of the input shape as the
              output dimension.

              Example::

              - input shape = (2,3,4), shape = (-3,4), output shape = (6,4)
              - input shape = (2,3,4,5), shape = (-3,-3), output shape = (6,20)
              - input shape = (2,3,4), shape = (0,-3), output shape = (2,12)
              - input shape = (2,3,4), shape = (-3,-2), output shape = (6,4)

            - ``-4`` split one dimension of the input into two dimensions passed subsequent to
              -4 in shape (can contain -1).

              Example::

              - input shape = (2,3,4), shape = (-4,1,2,-2), output shape =(1,2,3,4)
              - input shape = (2,3,4), shape = (2,-4,-1,3,-2), output shape = (2,1,3,4)

            - If the argument `reverse` is set to 1, then the special values are inferred from right
              to left.

              Example::

              - without reverse=1, for input shape = (10,5,4), shape = (-1,0), output shape would be \
                (40,5).
              - with reverse=1, output shape will be (50,4).

        reverse : bool, default False
            If true then the special values are inferred from right to left. Only supported as
            keyword argument.


        Returns
        -------
        NDArray
            An array with desired shape that shares data with this array.

        Examples
        --------
        >>> x = mx.nd.arange(0,6).reshape(2,3)
        >>> x.asnumpy()
        array([[ 0.,  1.,  2.],
               [ 3.,  4.,  5.]], dtype=float32)
        >>> y = x.reshape(3,2)
        >>> y.asnumpy()
        array([[ 0.,  1.],
               [ 2.,  3.],
               [ 4.,  5.]], dtype=float32)
        >>> y = x.reshape(3,-1)
        >>> y.asnumpy()
        array([[ 0.,  1.],
               [ 2.,  3.],
               [ 4.,  5.]], dtype=float32)
        >>> y = x.reshape(3,2)
        >>> y.asnumpy()
        array([[ 0.,  1.],
               [ 2.,  3.],
               [ 4.,  5.]], dtype=float32)
        >>> y = x.reshape(-3)
        >>> y.asnumpy()
        array([ 0.  1.  2.  3.  4.  5.], dtype=float32)
        >>> y[:] = -1
        >>> x.asnumpy()
        array([[-1., -1., -1.],
               [-1., -1., -1.]], dtype=float32)
        """
        if len(shape) == 1 and isinstance(shape[0], (list, tuple)):
            shape = shape[0]
        elif not shape:
            shape = kwargs.get('shape')
            assert shape, "Shape must be provided."
        if not all(k in ['shape', 'reverse'] for k in kwargs):
            raise TypeError(
                "Got unknown keywords in reshape: {}. " \
                "Accepted keyword arguments are 'shape' and 'reverse'.".format(
                    ', '.join([k for k in kwargs if k not in ['shape', 'reverse']])))
        reverse = kwargs.get('reverse', False)
        handle = NDArrayHandle()

        # Actual reshape
        check_call(_LIB.MXNDArrayReshape64(self.handle,
                                           len(shape),
                                           c_array(ctypes.c_int64, shape),
                                           reverse,
                                           ctypes.byref(handle)))
        return NDArray(handle=handle, writable=self.writable)
def reshape ( self , * shape , ** kwargs ) : if len ( shape ) == 1 and isinstance ( shape [ 0 ] , ( list , tuple ) ) : shape = shape [ 0 ] elif not shape : shape = kwargs . get ( 'shape' ) assert shape , "Shape must be provided." if not all ( k in [ 'shape' , 'reverse' ] for k in kwargs ) : raise TypeError ( "Got unknown keywords in reshape: {}. " "Accepted keyword arguments are 'shape' and 'reverse'." . format ( ', ' . join ( [ k for k in kwargs if k not in [ 'shape' , 'reverse' ] ] ) ) ) reverse = kwargs . get ( 'reverse' , False ) handle = NDArrayHandle ( ) check_call ( _LIB . MXNDArrayReshape64 ( self . handle , len ( shape ) , c_array ( ctypes . c_int64 , shape ) , reverse , ctypes . byref ( handle ) ) ) return NDArray ( handle = handle , writable = self . writable )
Returns a **view** of this array with a new shape without altering any data.

        Parameters
        ----------
        shape : tuple of int, or n ints
            The new shape should not change the array size, namely
            ``np.prod(new_shape)`` should be equal to ``np.prod(self.shape)``.
            Some dimensions of the shape can take special values from the set {0, -1, -2, -3, -4}.
            The significance of each is explained below:

            - ``0``  copy this dimension from the input to the output shape.

              Example::

              - input shape = (2,3,4), shape = (4,0,2), output shape = (4,3,2)
              - input shape = (2,3,4), shape = (2,0,0), output shape = (2,3,4)

            - ``-1`` infers the dimension of the output shape by using the remainder of the
              input dimensions keeping the size of the new array same as that of the input array.
              At most one dimension of shape can be -1.

              Example::

              - input shape = (2,3,4), shape = (6,1,-1), output shape = (6,1,4)
              - input shape = (2,3,4), shape = (3,-1,8), output shape = (3,1,8)
              - input shape = (2,3,4), shape=(-1,), output shape = (24,)

            - ``-2`` copy all/remainder of the input dimensions to the output shape.

              Example::

              - input shape = (2,3,4), shape = (-2,), output shape = (2,3,4)
              - input shape = (2,3,4), shape = (2,-2), output shape = (2,3,4)
              - input shape = (2,3,4), shape = (-2,1,1), output shape = (2,3,4,1,1)

            - ``-3`` use the product of two consecutive dimensions of the input shape as the
              output dimension.

              Example::

              - input shape = (2,3,4), shape = (-3,4), output shape = (6,4)
              - input shape = (2,3,4,5), shape = (-3,-3), output shape = (6,20)
              - input shape = (2,3,4), shape = (0,-3), output shape = (2,12)
              - input shape = (2,3,4), shape = (-3,-2), output shape = (6,4)

            - ``-4`` split one dimension of the input into two dimensions passed subsequent to
              -4 in shape (can contain -1).

              Example::

              - input shape = (2,3,4), shape = (-4,1,2,-2), output shape =(1,2,3,4)
              - input shape = (2,3,4), shape = (2,-4,-1,3,-2), output shape = (2,1,3,4)

            - If the argument `reverse` is set to 1, then the special values are inferred from right
              to left.

              Example::

              - without reverse=1, for input shape = (10,5,4), shape = (-1,0), output shape would be \
                (40,5).
              - with reverse=1, output shape will be (50,4).

        reverse : bool, default False
            If true then the special values are inferred from right to left. Only supported as
            keyword argument.


        Returns
        -------
        NDArray
            An array with desired shape that shares data with this array.

        Examples
        --------
        >>> x = mx.nd.arange(0,6).reshape(2,3)
        >>> x.asnumpy()
        array([[ 0.,  1.,  2.],
               [ 3.,  4.,  5.]], dtype=float32)
        >>> y = x.reshape(3,2)
        >>> y.asnumpy()
        array([[ 0.,  1.],
               [ 2.,  3.],
               [ 4.,  5.]], dtype=float32)
        >>> y = x.reshape(3,-1)
        >>> y.asnumpy()
        array([[ 0.,  1.],
               [ 2.,  3.],
               [ 4.,  5.]], dtype=float32)
        >>> y = x.reshape(3,2)
        >>> y.asnumpy()
        array([[ 0.,  1.],
               [ 2.,  3.],
               [ 4.,  5.]], dtype=float32)
        >>> y = x.reshape(-3)
        >>> y.asnumpy()
        array([ 0.  1.  2.  3.  4.  5.], dtype=float32)
        >>> y[:] = -1
        >>> x.asnumpy()
        array([[-1., -1., -1.],
               [-1., -1., -1.]], dtype=float32)