sklam · Apr 1, 2022 · Dec 15, 2021 · Dec 15, 2021 · Dec 15, 2021 · Dec 16, 2021
diff --git a/docs/source/reference/numpysupported.rst b/docs/source/reference/numpysupported.rst
@@ -435,6 +435,7 @@ The following top-level functions are supported:
 * :func:`numpy.bincount`
 * :func:`numpy.blackman`
 * :func:`numpy.broadcast_to` (only the 2 first arguments)
+* :func:`numpy.broadcast_arrays` (only the first argument)
 * :func:`numpy.column_stack`
 * :func:`numpy.concatenate`
 * :func:`numpy.convolve` (only the 2 first arguments)

diff --git a/numba/cpython/unsafe/tuple.py b/numba/cpython/unsafe/tuple.py
@@ -33,6 +33,36 @@ def codegen(context, builder, signature, args):
     return sig, codegen
 
 
+@intrinsic
+def build_int_tuple(tyctx, sz):
+    """Creates a sz-tuple of integers."""
+    if not isinstance(sz, types.IntegerLiteral):
+        raise errors.RequireLiteralValue(sz)
+
+    size = int(sz.literal_value)
+    tuple_type = types.UniTuple(dtype=types.intp, count=size)
+    sig = tuple_type(sz)
+
+    def codegen(context, builder, signature, args):
+        def impl(length, empty_tuple):
+            out = empty_tuple
+            for i in range(length):
+                out = tuple_setitem(out, i, 0)
+            return out
+
+        inner_argtypes = [types.intp, tuple_type]
+        inner_sig = typing.signature(tuple_type, *inner_argtypes)
+        ll_idx_type = context.get_value_type(types.intp)
+        # Allocate an empty tuple
+        empty_tuple = context.get_constant_undef(tuple_type)
+        inner_args = [ll_idx_type(size), empty_tuple]
+
+        res = context.compile_internal(builder, impl, inner_sig, inner_args)
+        return res
+
+    return sig, codegen
+
+
 @intrinsic
 def build_full_slice_tuple(tyctx, sz):
     """Creates a sz-tuple of full slices."""

diff --git a/numba/np/arrayobj.py b/numba/np/arrayobj.py
@@ -30,7 +30,9 @@
                                   intrinsic)
 from numba.misc import quicksort, mergesort
 from numba.cpython import slicing
-from numba.cpython.unsafe.tuple import tuple_setitem, build_full_slice_tuple
+from numba.cpython.unsafe.tuple import (build_int_tuple,
+                                        tuple_setitem,
+                                        build_full_slice_tuple)
 from numba.core.overload_glue import glue_lowering
 from numba.core.extending import overload_classmethod
 
@@ -1382,6 +1384,49 @@ def impl(array, shape):
     return impl
 
 
+@register_jitable
+def numpy_broadcast_shapes_list(r, m, shape):
+    for i in range(len(shape)):
+        k = m - len(shape) + i
+        tmp = shape[i]
+        if tmp == 1:
+            continue
+        if r[k] == 1:
+            r[k] = tmp
+        elif r[k] != tmp:
+            raise ValueError("shape mismatch: objects"
+                             " cannot be broadcast"
+                             " to a single shape")
+
+
+@overload(np.broadcast_arrays)
+def numpy_broadcast_arrays(*args):
+    # number of dimensions
+    m = max([arr.ndim for arr in args])
+
+    def impl(*args):
+        # find out the output shape
+        # we can't call np.broadcast_shapes here since args may have arrays
+        # with different shapes and it is not possible to create a list
+        # with those shapes dinamically
+        shape = [1] * m
+        for array in literal_unroll(args):
+            numpy_broadcast_shapes_list(shape, m, array.shape)
+
+        tup = build_int_tuple(m)
+
+        for i in range(m):
+            tup = tuple_setitem(tup, i, shape[i])
+
+        # numpy checks if the input arrays have the same shape as `shape`
+        outs = []
+        for array in literal_unroll(args):
+            outs.append(np.broadcast_to(array, tup))
+        return outs
+
+    return impl
+
+
 def fancy_setslice(context, builder, sig, args, index_types, indices):
     """
     Implement slice assignment for arrays.  This implementation works for