Implemented tensor.reshape_as

ivy/functional/frontends/torch/tensor.py

@@ -61,6 +61,10 @@ def reshape(self, *args, shape=None):
                 return torch_frontend.reshape(self._ivy_array, args)
         return torch_frontend.reshape(self._ivy_array)
 
+    @with_unsupported_dtypes({"1.11.0 and below": ("bfloat16",)}, "torch")
+    def reshape_as(self, other):
+        return torch_frontend.reshape(self, other.shape)
+
     @with_unsupported_dtypes({"1.11.0 and below": ("bfloat16",)}, "torch")
     def add(self, other, *, alpha=1):
         return torch_frontend.add(self._ivy_array, other, alpha=alpha)

ivy_tests/test_ivy/test_frontends/test_torch/test_tensor.py

@@ -442,6 +442,45 @@ def test_torch_instance_reshape(
     )
 
 
+# reshape_as
+@handle_frontend_method(
+    class_tree=CLASS_TREE,
+    init_tree="torch.tensor",
+    method_name="reshape_as",
+    dtype_x=helpers.dtype_and_values(
+        available_dtypes=helpers.get_dtypes("valid", full=True), num_arrays=2
+    ),
+)
+def test_torch_instance_reshape_as(
+    dtype_x,
+    init_num_positional_args: pf.NumPositionalArgFn,
+    method_num_positional_args: pf.NumPositionalArgMethod,
+    as_variable: pf.AsVariableFlags,
+    native_array: pf.NativeArrayFlags,
+    frontend_method_data,
+    frontend,
+):
+    input_dtype, x = dtype_x
+    helpers.test_frontend_method(
+        init_input_dtypes=input_dtype,
+        init_as_variable_flags=as_variable,
+        init_num_positional_args=init_num_positional_args,
+        init_native_array_flags=native_array,
+        init_all_as_kwargs_np={
+            "data": x[0],
+        },
+        method_input_dtypes=input_dtype,
+        method_as_variable_flags=as_variable,
+        method_num_positional_args=method_num_positional_args,
+        method_native_array_flags=native_array,
+        method_all_as_kwargs_np={
+            "other": x[1],
+        },
+        frontend_method_data=frontend_method_data,
+        frontend=frontend,
+    )
+
+
 # sin
 @handle_frontend_method(
     class_tree=CLASS_TREE,