Add frombuffer to Torch frontend (ivy-llc#14109)

Co-authored-by: Vansh Gupta <[email protected]>

ivy/functional/frontends/torch/creation_ops.py

@@ -285,3 +285,15 @@ def asarray(
     copy=None,
 ):
     return ivy.asarray(obj, copy=copy, dtype=dtype, device=device)
+
+
+@to_ivy_arrays_and_back
+def frombuffer(
+    buffer, 
+    *, 
+    dtype,
+    count=-1,
+    offset=0,
+    requires_grad=False,
+):
+    return ivy.frombuffer(buffer, dtype=dtype, count=count, offset=offset)

ivy_tests/test_ivy/test_frontends/test_torch/test_creation_ops.py

@@ -2,6 +2,7 @@
 import ivy
 from hypothesis import strategies as st, assume
 import math
+import numpy as np
 
 # local
 import ivy_tests.test_ivy.helpers as helpers
@@ -688,3 +689,48 @@ def test_torch_from_dlpack(
         fn_tree=fn_tree,
         on_device=on_device,
     )
+
+
+@st.composite
+def _get_dtype_buffer_count_offset(draw):
+    dtype, value = draw(
+        helpers.dtype_and_values(
+            available_dtypes=helpers.get_dtypes("valid"),
+        )
+    )
+    value = np.array(value)
+    length = value.size
+    value = value.tobytes()
+
+    offset = draw(helpers.ints(min_value=0, max_value=length - 1))
+    count = draw(helpers.ints(min_value=-(2**30), max_value=length - offset))
+    if count == 0:
+        count = -1
+    offset = offset * np.dtype(dtype[0]).itemsize
+
+    return dtype, value, count, offset
+
+
+@handle_frontend_test(
+    fn_tree="torch.frombuffer",
+    dtype_buffer_count_offset=_get_dtype_buffer_count_offset(),
+)
+def test_torch_frombuffer(
+    dtype_buffer_count_offset,
+    test_flags,
+    frontend,
+    fn_tree,
+    on_device,
+):
+    input_dtype, buffer, count, offset = dtype_buffer_count_offset
+    helpers.test_frontend_function(
+        input_dtypes=input_dtype,
+        test_flags=test_flags,
+        on_device=on_device,
+        frontend=frontend,
+        fn_tree=fn_tree,
+        buffer=buffer,
+        dtype=input_dtype[0],
+        count=count,
+        offset=offset,
+    )