Implemented torch.Tensor.__and__

ivy/functional/frontends/torch/tensor.py

@@ -863,6 +863,9 @@ def __or__(self, other):
     def __invert__(self):
         return torch_frontend.bitwise_not(self._ivy_array)
 
+    def __and__(self, other):
+        return torch_frontend.bitwise_and(self, other)
+
     # Method aliases
     absolute, absolute_ = abs, abs_
     ndimension = dim

ivy_tests/test_ivy/test_frontends/test_torch/test_tensor.py

@@ -5695,3 +5695,42 @@ def test_torch_instance_bitwise_and_(
         frontend=frontend,
         on_device=on_device,
     )
+
+
+# __and__
+@handle_frontend_method(
+    class_tree=CLASS_TREE,
+    init_tree="torch.tensor",
+    method_name="__and__",
+    dtype_and_x=helpers.dtype_and_values(
+        available_dtypes=st.one_of(st.just(("bool",)), helpers.get_dtypes("integer")),
+        num_arrays=2,
+        min_value=-1e04,
+        max_value=1e04,
+        allow_inf=False,
+    ),
+)
+def test_torch_special_and(
+    dtype_and_x,
+    frontend_method_data,
+    init_flags,
+    method_flags,
+    frontend,
+    on_device,
+):
+    input_dtype, x = dtype_and_x
+    helpers.test_frontend_method(
+        init_input_dtypes=input_dtype,
+        init_all_as_kwargs_np={
+            "data": x[0],
+        },
+        method_input_dtypes=input_dtype,
+        method_all_as_kwargs_np={
+            "other": x[1],
+        },
+        frontend_method_data=frontend_method_data,
+        init_flags=init_flags,
+        method_flags=method_flags,
+        frontend=frontend,
+        on_device=on_device,
+    )