Merge pull request #23586 from rajasekharporeddy:testbranch3

PiperOrigin-RevId: 673976796

jax/_src/numpy/lax_numpy.py

@@ -1774,9 +1774,40 @@ def unravel_index(indices: ArrayLike, shape: Shape) -> tuple[Array, ...]:
   return tuple(where(oob_pos, s - 1, where(oob_neg, 0, i))
                for s, i in safe_zip(shape, out_indices))
 
-@util.implements(np.resize)
+
 @partial(jit, static_argnames=('new_shape',))
 def resize(a: ArrayLike, new_shape: Shape) -> Array:
+  """Return a new array with specified shape.
+
+  JAX implementation of :func:`numpy.resize`.
+
+  Args:
+    a: input array or scalar.
+    new_shape: int or tuple of ints. Specifies the shape of the resized array.
+
+  Returns:
+    A resized array with specified shape. The elements of ``a`` are repeated in
+    the resized array, if the resized array is larger than the original aray.
+
+  See also:
+    - :func:`jax.numpy.reshape`: Returns a reshaped copy of an array.
+    - :func:`jax.numpy.repeat`: Constructs an array from repeated elements.
+
+  Examples:
+    >>> x = jnp.array([1, 2, 3, 4, 5, 6, 7, 8, 9])
+    >>> jnp.resize(x, (3, 3))
+    Array([[1, 2, 3],
+           [4, 5, 6],
+           [7, 8, 9]], dtype=int32)
+    >>> jnp.resize(x, (3, 4))
+    Array([[1, 2, 3, 4],
+           [5, 6, 7, 8],
+           [9, 1, 2, 3]], dtype=int32)
+    >>> jnp.resize(4, (3, 2))
+    Array([[4, 4],
+           [4, 4],
+           [4, 4]], dtype=int32, weak_type=True)
+  """
   util.check_arraylike("resize", a)
   new_shape = _ensure_index_tuple(new_shape)