feat: added broadcast_tensors funtion for torch frontend

ivy/functional/frontends/torch/tensor_functions.py

@@ -4,6 +4,11 @@
 from ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back
 
 
+@to_ivy_arrays_and_back
+def broadcast_tensors(*tensors):
+    return ivy.broadcast_arrays(*tensors)
+
+
 @to_ivy_arrays_and_back
 def is_complex(input):
     return ivy.is_complex_dtype(input)

ivy_tests/test_ivy/test_frontends/test_torch/test_tensor_functions.py

@@ -10,6 +10,40 @@
 )
 
 
+# broadcast_tensors
+@handle_frontend_test(
+    fn_tree="torch.broadcast_tensors",
+    dtype_and_x=helpers.dtype_and_values(
+        available_dtypes=helpers.get_dtypes("valid"),
+        min_num_dims=1,
+        num_arrays=2,
+    ),
+)
+def test_torch_broadcast_tensors(
+    *,
+    dtype_and_x,
+    on_device,
+    fn_tree,
+    frontend,
+    test_flags,
+    backend_fw,
+):
+    input_dtype, x = dtype_and_x
+    kw = {}
+    for i, array in enumerate(x):
+        kw[f"x{i}"] = array
+    test_flags.num_positional_args = len(kw)
+    helpers.test_frontend_function(
+        input_dtypes=input_dtype,
+        backend_to_test=backend_fw,
+        on_device=on_device,
+        frontend=frontend,
+        test_flags=test_flags,
+        fn_tree=fn_tree,
+        **kw,
+    )
+
+
 @handle_frontend_test(
     fn_tree="torch.is_complex",
     dtype_and_x=helpers.dtype_and_values(