added add function to PyTorch frontend, with corresponding unit test.

ivy/functional/frontends/torch/pointwise_ops.py

@@ -1,7 +1,16 @@
 # global
 import ivy
 
+
+def add(input, other, *, alpha=1, out=None):
+    return ivy.add(input, other*alpha, out=out)
+
+
+add.unsupported_dtypes = ('float16',)
+
+
 def tan(input, *, out=None):
     return ivy.tan(input, out=out)
 
+
 tan.unsupported_dtypes = ('float16',)

ivy_tests/test_ivy/test_frontends/test_torch/test_pointwise_ops.py

@@ -8,6 +8,44 @@
 import ivy.functional.backends.torch as ivy_torch
 
 
+# add
+@given(
+    dtype_and_x=helpers.dtype_and_values(
+        tuple(set(ivy_np.valid_float_dtypes).intersection(
+              set(ivy_torch.valid_float_dtypes))), 2),
+    alpha=st.floats(),
+    as_variable=st.booleans(),
+    with_out=st.booleans(),
+    num_positional_args=helpers.num_positional_args(
+        fn_name="functional.frontends.torch.add"),
+    native_array=st.booleans(),
+)
+def test_torch_add(
+    dtype_and_x,
+    alpha,
+    as_variable,
+    with_out,
+    num_positional_args,
+    native_array,
+    fw,
+):
+    input_dtype, x = dtype_and_x
+    helpers.test_frontend_function(
+        input_dtype,
+        as_variable,
+        with_out,
+        num_positional_args,
+        native_array,
+        fw,
+        "torch",
+        "add",
+        input=np.asarray(x[0], dtype=input_dtype[0]),
+        other=np.asarray(x[1], dtype=input_dtype[1]),
+        alpha=alpha,
+        out=None,
+    )
+
+
 # tan
 @given(
     dtype_and_x=helpers.dtype_and_values(