add torch tensor maximum instance method with its test

ivy/functional/frontends/torch/tensor.py

@@ -765,6 +765,22 @@ def long(self, memory_format=None):
     def max(self, dim=None, keepdim=False):
         return torch_frontend.max(self, dim=dim, keepdim=keepdim)
 
+    @with_unsupported_dtypes(
+        {
+            "2.0.1 and below": (
+                "complex",
+                "bfloat16",
+                "bool",
+                "uint16",
+                "uint32",
+                "uint64",
+            )
+        },
+        "torch",
+    )
+    def maximum(self, other, *, out=None):
+        return torch_frontend.maximum(self, other=other, out=out)
+
     @property
     def is_quantized(self):
         return "q" in ivy.dtype(self.ivy_array)

ivy_tests/test_ivy/test_frontends/test_torch/test_tensor.py

@@ -8662,6 +8662,44 @@ def test_torch_tensor_max(
     )
 
 
+# maximum
+@handle_frontend_method(
+    class_tree=CLASS_TREE,
+    init_tree="torch.tensor",
+    method_name="maximum",
+    dtype_and_x=helpers.dtype_and_values(
+        available_dtypes=helpers.get_dtypes("valid"),
+        num_arrays=2,
+    ),
+)
+def test_torch_tensor_maximum(
+    dtype_and_x,
+    frontend_method_data,
+    init_flags,
+    method_flags,
+    frontend,
+    on_device,
+    backend_fw,
+):
+    input_dtype, x = dtype_and_x
+    helpers.test_frontend_method(
+        init_input_dtypes=input_dtype,
+        backend_to_test=backend_fw,
+        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,
+    )
+
+
 # mean
 @handle_frontend_method(
     class_tree=CLASS_TREE,