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zhqQ2W{LqxS)G%kwt$@J#>?6~G90FWh_1c{9n=Xq)+(eC4|G-@pXX$RlMfVs{Uei1&eT@C`&qwQd?u?&Hl5w}-FRnDXz}yV`a@;QUPwdt;`#O0f%mMGp{#sMQ5kC?WH=WNznBuAfT;E#oW

-ivy.arg_info(fn, *, name=None, idx=None)[source]#
+ivy.arg_info(fn, *, name=None, idx=None)[source]#

Return the index and inspect.Parameter representation of the specified argument. In the form of a dict with keys “idx” and “param”.

diff --git a/docs/functional/ivy/general/ivy.functional.ivy.general.function_supported_devices_and_dtypes.html b/docs/functional/ivy/general/ivy.functional.ivy.general.function_supported_devices_and_dtypes.html index 93817ba3..b692af66 100644 --- a/docs/functional/ivy/general/ivy.functional.ivy.general.function_supported_devices_and_dtypes.html +++ b/docs/functional/ivy/general/ivy.functional.ivy.general.function_supported_devices_and_dtypes.html @@ -1411,7 +1411,7 @@

function_supported_devices_and_dtypes#

-ivy.function_supported_devices_and_dtypes(fn, recurse=True)[source]#
+ivy.function_supported_devices_and_dtypes(fn, recurse=True)[source]#

Return the supported combination of devices and dtypes of the current backend’s function. The function returns a dict containing the supported combination of devices and dtypes of the primary and compositional diff --git a/docs/functional/ivy/general/ivy.functional.ivy.general.function_unsupported_devices_and_dtypes.html b/docs/functional/ivy/general/ivy.functional.ivy.general.function_unsupported_devices_and_dtypes.html index 516b5e8f..b4f1cf91 100644 --- a/docs/functional/ivy/general/ivy.functional.ivy.general.function_unsupported_devices_and_dtypes.html +++ b/docs/functional/ivy/general/ivy.functional.ivy.general.function_unsupported_devices_and_dtypes.html @@ -1411,7 +1411,7 @@

function_unsupported_devices_and_dtypes#

-ivy.function_unsupported_devices_and_dtypes(fn, recurse=True)[source]#
+ivy.function_unsupported_devices_and_dtypes(fn, recurse=True)[source]#

Return the unsupported combination of devices and dtypes of the current backend’s function. The function returns a dict containing the unsupported combination of devices and dtypes of the primary and compositional diff --git a/docs/functional/ivy/general/ivy.functional.ivy.general.gather.html b/docs/functional/ivy/general/ivy.functional.ivy.general.gather.html index 88e3a6df..9d8a1560 100644 --- a/docs/functional/ivy/general/ivy.functional.ivy.general.gather.html +++ b/docs/functional/ivy/general/ivy.functional.ivy.general.gather.html @@ -1411,7 +1411,7 @@

gather#

-ivy.gather(params, indices, /, *, axis=-1, batch_dims=0, out=None)[source]#
+ivy.gather(params, indices, /, *, axis=-1, batch_dims=0, out=None)[source]#

Gather slices from params at axis according to indices.

Parameters:
diff --git a/docs/functional/ivy/general/ivy.functional.ivy.general.gather_nd.html b/docs/functional/ivy/general/ivy.functional.ivy.general.gather_nd.html index bbe74ea7..8b1f953b 100644 --- a/docs/functional/ivy/general/ivy.functional.ivy.general.gather_nd.html +++ b/docs/functional/ivy/general/ivy.functional.ivy.general.gather_nd.html @@ -1411,7 +1411,7 @@

gather_nd#

-ivy.gather_nd(params, indices, /, *, batch_dims=0, out=None)[source]#
+ivy.gather_nd(params, indices, /, *, batch_dims=0, out=None)[source]#

Gather slices from params into a array with shape specified by indices.

Parameters:
diff --git a/docs/functional/ivy/general/ivy.functional.ivy.general.get_num_dims.html b/docs/functional/ivy/general/ivy.functional.ivy.general.get_num_dims.html index 837cd237..afc846c2 100644 --- a/docs/functional/ivy/general/ivy.functional.ivy.general.get_num_dims.html +++ b/docs/functional/ivy/general/ivy.functional.ivy.general.get_num_dims.html @@ -1411,7 +1411,7 @@

get_num_dims#

-ivy.get_num_dims(x, /, *, as_array=False)[source]#
+ivy.get_num_dims(x, /, *, as_array=False)[source]#

Return the number of dimensions of the array x.

Parameters:
diff --git a/docs/functional/ivy/general/ivy.functional.ivy.general.inplace_decrement.html b/docs/functional/ivy/general/ivy.functional.ivy.general.inplace_decrement.html index 7817be57..58b97c59 100644 --- a/docs/functional/ivy/general/ivy.functional.ivy.general.inplace_decrement.html +++ b/docs/functional/ivy/general/ivy.functional.ivy.general.inplace_decrement.html @@ -1411,7 +1411,7 @@

inplace_decrement#

-ivy.inplace_decrement(x, val)[source]#
+ivy.inplace_decrement(x, val)[source]#

Perform in-place decrement for the input array.

Parameters:
diff --git a/docs/functional/ivy/general/ivy.functional.ivy.general.inplace_increment.html b/docs/functional/ivy/general/ivy.functional.ivy.general.inplace_increment.html index 431eae2d..36057cdb 100644 --- a/docs/functional/ivy/general/ivy.functional.ivy.general.inplace_increment.html +++ b/docs/functional/ivy/general/ivy.functional.ivy.general.inplace_increment.html @@ -1411,7 +1411,7 @@

inplace_increment#

-ivy.inplace_increment(x, val)[source]#
+ivy.inplace_increment(x, val)[source]#

Perform in-place increment for the input array.

Parameters:
diff --git a/docs/functional/ivy/general/ivy.functional.ivy.general.inplace_update.html b/docs/functional/ivy/general/ivy.functional.ivy.general.inplace_update.html index 8e4abcb6..122166ab 100644 --- a/docs/functional/ivy/general/ivy.functional.ivy.general.inplace_update.html +++ b/docs/functional/ivy/general/ivy.functional.ivy.general.inplace_update.html @@ -1411,7 +1411,7 @@

inplace_update#

-ivy.inplace_update(x, val, /, *, ensure_in_backend=False, keep_input_dtype=False)[source]#
+ivy.inplace_update(x, val, /, *, ensure_in_backend=False, keep_input_dtype=False)[source]#

Perform in-place update for the input array.

This will always be performed on ivy.Array instances pass in the input, and will also be performed on the native array classes in the backend when the backend diff --git a/docs/functional/ivy/general/ivy.functional.ivy.general.is_ivy_nested_array.html b/docs/functional/ivy/general/ivy.functional.ivy.general.is_ivy_nested_array.html index 37971cc7..9566f3d1 100644 --- a/docs/functional/ivy/general/ivy.functional.ivy.general.is_ivy_nested_array.html +++ b/docs/functional/ivy/general/ivy.functional.ivy.general.is_ivy_nested_array.html @@ -1411,7 +1411,7 @@

is_ivy_nested_array#

-ivy.is_ivy_nested_array(x, /)[source]#
+ivy.is_ivy_nested_array(x, /)[source]#

Determine whether the input x is an Ivy Nested Array.

Parameters:
diff --git a/docs/functional/ivy/general/ivy.functional.ivy.general.isin.html b/docs/functional/ivy/general/ivy.functional.ivy.general.isin.html index 789fd917..f654c815 100644 --- a/docs/functional/ivy/general/ivy.functional.ivy.general.isin.html +++ b/docs/functional/ivy/general/ivy.functional.ivy.general.isin.html @@ -1411,7 +1411,7 @@

isin#

-ivy.isin(elements, test_elements, /, *, assume_unique=False, invert=False)[source]#
+ivy.isin(elements, test_elements, /, *, assume_unique=False, invert=False)[source]#

Test if each element of elements is in test_elements.

Parameters:
diff --git a/docs/functional/ivy/general/ivy.functional.ivy.general.itemsize.html b/docs/functional/ivy/general/ivy.functional.ivy.general.itemsize.html index 1b5df342..cefe31b7 100644 --- a/docs/functional/ivy/general/ivy.functional.ivy.general.itemsize.html +++ b/docs/functional/ivy/general/ivy.functional.ivy.general.itemsize.html @@ -1411,7 +1411,7 @@

itemsize#

-ivy.itemsize(x, /)[source]#
+ivy.itemsize(x, /)[source]#

Return the size of the input array’s elements.

Parameters:
diff --git a/docs/functional/ivy/general/ivy.functional.ivy.general.multiprocessing.html b/docs/functional/ivy/general/ivy.functional.ivy.general.multiprocessing.html index 0a88a1a8..6a104825 100644 --- a/docs/functional/ivy/general/ivy.functional.ivy.general.multiprocessing.html +++ b/docs/functional/ivy/general/ivy.functional.ivy.general.multiprocessing.html @@ -1411,7 +1411,7 @@

multiprocessing#

-ivy.multiprocessing(context=None)[source]#
+ivy.multiprocessing(context=None)[source]#

Return backend-specific multiprocessing module.

Parameters:
diff --git a/docs/functional/ivy/general/ivy.functional.ivy.general.scatter_flat.html b/docs/functional/ivy/general/ivy.functional.ivy.general.scatter_flat.html index d741b1f6..ec2ccfda 100644 --- a/docs/functional/ivy/general/ivy.functional.ivy.general.scatter_flat.html +++ b/docs/functional/ivy/general/ivy.functional.ivy.general.scatter_flat.html @@ -1411,7 +1411,7 @@

scatter_flat#

-ivy.scatter_flat(indices, updates, /, *, size=None, reduction='sum', out=None)[source]#
+ivy.scatter_flat(indices, updates, /, *, size=None, reduction='sum', out=None)[source]#

Scatter flat updates into a new flat array according to flat indices.

Parameters:
diff --git a/docs/functional/ivy/general/ivy.functional.ivy.general.scatter_nd.html b/docs/functional/ivy/general/ivy.functional.ivy.general.scatter_nd.html index 290d5b19..8691bc93 100644 --- a/docs/functional/ivy/general/ivy.functional.ivy.general.scatter_nd.html +++ b/docs/functional/ivy/general/ivy.functional.ivy.general.scatter_nd.html @@ -1411,7 +1411,7 @@

scatter_nd#

-ivy.scatter_nd(indices, updates, /, shape=None, *, reduction='sum', out=None)[source]#
+ivy.scatter_nd(indices, updates, /, shape=None, *, reduction='sum', out=None)[source]#

Scatter updates into a new array according to indices.

Parameters:
diff --git a/docs/functional/ivy/general/ivy.functional.ivy.general.set_inplace_mode.html b/docs/functional/ivy/general/ivy.functional.ivy.general.set_inplace_mode.html index 79acad99..652b1c55 100644 --- a/docs/functional/ivy/general/ivy.functional.ivy.general.set_inplace_mode.html +++ b/docs/functional/ivy/general/ivy.functional.ivy.general.set_inplace_mode.html @@ -1411,7 +1411,7 @@

set_inplace_mode#

-ivy.set_inplace_mode(mode='lenient')[source]#
+ivy.set_inplace_mode(mode='lenient')[source]#

Set the memory management behavior for in-place updates in Ivy.

By default, Ivy creates new arrays in the backend for in-place updates. However, this behavior can be controlled by the user diff --git a/docs/functional/ivy/general/ivy.functional.ivy.general.set_item.html b/docs/functional/ivy/general/ivy.functional.ivy.general.set_item.html index 3a0100f0..05f6a610 100644 --- a/docs/functional/ivy/general/ivy.functional.ivy.general.set_item.html +++ b/docs/functional/ivy/general/ivy.functional.ivy.general.set_item.html @@ -1411,7 +1411,7 @@

set_item#

-ivy.set_item(x, query, val, /, *, copy=False)[source]#
+ivy.set_item(x, query, val, /, *, copy=False)[source]#

Replace slices of x (defined by query) with val, identical to x[query] = val.

diff --git a/docs/functional/ivy/general/ivy.functional.ivy.general.set_shape_array_mode.html b/docs/functional/ivy/general/ivy.functional.ivy.general.set_shape_array_mode.html index 488a17ac..c2e386b3 100644 --- a/docs/functional/ivy/general/ivy.functional.ivy.general.set_shape_array_mode.html +++ b/docs/functional/ivy/general/ivy.functional.ivy.general.set_shape_array_mode.html @@ -1411,7 +1411,7 @@

set_shape_array_mode#

-ivy.set_shape_array_mode(mode)[source]#
+ivy.set_shape_array_mode(mode)[source]#

Set the mode of returning shape as ivy.Array to the given mode instance.

Return type:
diff --git a/docs/functional/ivy/general/ivy.functional.ivy.general.shape.html b/docs/functional/ivy/general/ivy.functional.ivy.general.shape.html index 48b600a0..3c002f79 100644 --- a/docs/functional/ivy/general/ivy.functional.ivy.general.shape.html +++ b/docs/functional/ivy/general/ivy.functional.ivy.general.shape.html @@ -1411,7 +1411,7 @@

shape#

-ivy.shape(x, /, *, as_array=False)[source]#
+ivy.shape(x, /, *, as_array=False)[source]#

Return the shape of the array x.

Parameters:
diff --git a/docs/functional/ivy/general/ivy.functional.ivy.general.size.html b/docs/functional/ivy/general/ivy.functional.ivy.general.size.html index 8a39d0aa..f039ab14 100644 --- a/docs/functional/ivy/general/ivy.functional.ivy.general.size.html +++ b/docs/functional/ivy/general/ivy.functional.ivy.general.size.html @@ -1411,7 +1411,7 @@

size#

-ivy.size(x)[source]#
+ivy.size(x)[source]#

Return the number of elements of the array x.

Parameters:
diff --git a/docs/functional/ivy/general/ivy.functional.ivy.general.strides.html b/docs/functional/ivy/general/ivy.functional.ivy.general.strides.html index 59e08bc7..ef7871ca 100644 --- a/docs/functional/ivy/general/ivy.functional.ivy.general.strides.html +++ b/docs/functional/ivy/general/ivy.functional.ivy.general.strides.html @@ -1411,7 +1411,7 @@

strides#

-ivy.strides(x, /)[source]#
+ivy.strides(x, /)[source]#

Return the input array’s strides across each dimension.

Parameters:
diff --git a/docs/functional/ivy/general/ivy.functional.ivy.general.unset_inplace_mode.html b/docs/functional/ivy/general/ivy.functional.ivy.general.unset_inplace_mode.html index db17150d..1be0719d 100644 --- a/docs/functional/ivy/general/ivy.functional.ivy.general.unset_inplace_mode.html +++ b/docs/functional/ivy/general/ivy.functional.ivy.general.unset_inplace_mode.html @@ -1411,7 +1411,7 @@

unset_inplace_mode#

-ivy.unset_inplace_mode()[source]#
+ivy.unset_inplace_mode()[source]#

Reset the memory management behavior for in-place updates in Ivy to the previous state.

diff --git a/docs/functional/ivy/general/ivy.functional.ivy.general.unset_shape_array_mode.html b/docs/functional/ivy/general/ivy.functional.ivy.general.unset_shape_array_mode.html index 3fe450fc..91ec0273 100644 --- a/docs/functional/ivy/general/ivy.functional.ivy.general.unset_shape_array_mode.html +++ b/docs/functional/ivy/general/ivy.functional.ivy.general.unset_shape_array_mode.html @@ -1411,7 +1411,7 @@

unset_shape_array_mode#

-ivy.unset_shape_array_mode()[source]#
+ivy.unset_shape_array_mode()[source]#

Reset the mode of returning shape as ivy.Array to the previous state.

Return type:
diff --git a/docs/functional/ivy/general/ivy.functional.ivy.general.vmap.html b/docs/functional/ivy/general/ivy.functional.ivy.general.vmap.html index 735d27bf..0d0d7117 100644 --- a/docs/functional/ivy/general/ivy.functional.ivy.general.vmap.html +++ b/docs/functional/ivy/general/ivy.functional.ivy.general.vmap.html @@ -1411,7 +1411,7 @@

vmap#

-ivy.vmap(func, in_axes=0, out_axes=0)[source]#
+ivy.vmap(func, in_axes=0, out_axes=0)[source]#

Vectorizing map. Creates a function which maps func over argument axes.

Parameters:
diff --git a/docs/functional/ivy/ivy.functional.ivy.general.html b/docs/functional/ivy/ivy.functional.ivy.general.html index 3673dbf6..ddf66306 100644 --- a/docs/functional/ivy/ivy.functional.ivy.general.html +++ b/docs/functional/ivy/ivy.functional.ivy.general.html @@ -1475,7 +1475,7 @@

General#<
-ivy.arg_info(fn, *, name=None, idx=None)[source]#
+ivy.arg_info(fn, *, name=None, idx=None)[source]#

Return the index and inspect.Parameter representation of the specified argument. In the form of a dict with keys “idx” and “param”.

@@ -2267,7 +2267,7 @@

General#<
-ivy.function_supported_devices_and_dtypes(fn, recurse=True)[source]#
+ivy.function_supported_devices_and_dtypes(fn, recurse=True)[source]#

Return the supported combination of devices and dtypes of the current backend’s function. The function returns a dict containing the supported combination of devices and dtypes of the primary and compositional @@ -2291,7 +2291,7 @@

General#<
-ivy.function_unsupported_devices_and_dtypes(fn, recurse=True)[source]#
+ivy.function_unsupported_devices_and_dtypes(fn, recurse=True)[source]#

Return the unsupported combination of devices and dtypes of the current backend’s function. The function returns a dict containing the unsupported combination of devices and dtypes of the primary and compositional @@ -2315,7 +2315,7 @@

General#<
-ivy.gather(params, indices, /, *, axis=-1, batch_dims=0, out=None)[source]#
+ivy.gather(params, indices, /, *, axis=-1, batch_dims=0, out=None)[source]#

Gather slices from params at axis according to indices.

Parameters:
@@ -2407,7 +2407,7 @@

General#<
-ivy.gather_nd(params, indices, /, *, batch_dims=0, out=None)[source]#
+ivy.gather_nd(params, indices, /, *, batch_dims=0, out=None)[source]#

Gather slices from params into a array with shape specified by indices.

Parameters:
@@ -2528,7 +2528,7 @@

General#<
-ivy.get_num_dims(x, /, *, as_array=False)[source]#
+ivy.get_num_dims(x, /, *, as_array=False)[source]#

Return the number of dimensions of the array x.

Parameters:
@@ -2689,7 +2689,7 @@

General#<
-ivy.inplace_decrement(x, val)[source]#
+ivy.inplace_decrement(x, val)[source]#

Perform in-place decrement for the input array.

Parameters:
@@ -2752,7 +2752,7 @@

General#<
-ivy.inplace_increment(x, val)[source]#
+ivy.inplace_increment(x, val)[source]#

Perform in-place increment for the input array.

Parameters:
@@ -2802,7 +2802,7 @@

General#<
-ivy.inplace_update(x, val, /, *, ensure_in_backend=False, keep_input_dtype=False)[source]#
+ivy.inplace_update(x, val, /, *, ensure_in_backend=False, keep_input_dtype=False)[source]#

Perform in-place update for the input array.

This will always be performed on ivy.Array instances pass in the input, and will also be performed on the native array classes in the backend when the backend @@ -2996,7 +2996,7 @@

General#<
-ivy.is_ivy_nested_array(x, /)[source]#
+ivy.is_ivy_nested_array(x, /)[source]#

Determine whether the input x is an Ivy Nested Array.

Parameters:
@@ -3045,7 +3045,7 @@

General#<
-ivy.isin(elements, test_elements, /, *, assume_unique=False, invert=False)[source]#
+ivy.isin(elements, test_elements, /, *, assume_unique=False, invert=False)[source]#

Test if each element of elements is in test_elements.

Parameters:
@@ -3093,7 +3093,7 @@

General#<
-ivy.itemsize(x, /)[source]#
+ivy.itemsize(x, /)[source]#

Return the size of the input array’s elements.

Parameters:
@@ -3158,7 +3158,7 @@

General#<
-ivy.multiprocessing(context=None)[source]#
+ivy.multiprocessing(context=None)[source]#

Return backend-specific multiprocessing module.

Parameters:
@@ -3235,7 +3235,7 @@

General#<
-ivy.scatter_flat(indices, updates, /, *, size=None, reduction='sum', out=None)[source]#
+ivy.scatter_flat(indices, updates, /, *, size=None, reduction='sum', out=None)[source]#

Scatter flat updates into a new flat array according to flat indices.

Parameters:
@@ -3304,7 +3304,7 @@

General#<
-ivy.scatter_nd(indices, updates, /, shape=None, *, reduction='sum', out=None)[source]#
+ivy.scatter_nd(indices, updates, /, shape=None, *, reduction='sum', out=None)[source]#

Scatter updates into a new array according to indices.

Parameters:
@@ -3456,7 +3456,7 @@

Parameter#
-ivy.set_inplace_mode(mode='lenient')[source]#
+ivy.set_inplace_mode(mode='lenient')[source]#

Set the memory management behavior for in-place updates in Ivy.

By default, Ivy creates new arrays in the backend for in-place updates. However, this behavior can be controlled by the user @@ -3506,7 +3506,7 @@

Parameter#
-ivy.set_item(x, query, val, /, *, copy=False)[source]#
+ivy.set_item(x, query, val, /, *, copy=False)[source]#

Replace slices of x (defined by query) with val, identical to x[query] = val.

@@ -3695,7 +3695,7 @@

Parameter#
-ivy.set_shape_array_mode(mode)[source]#
+ivy.set_shape_array_mode(mode)[source]#

Set the mode of returning shape as ivy.Array to the given mode instance.

Return type:
@@ -3784,7 +3784,7 @@

Parameter#
-ivy.shape(x, /, *, as_array=False)[source]#
+ivy.shape(x, /, *, as_array=False)[source]#

Return the shape of the array x.

Parameters:
@@ -3817,7 +3817,7 @@

Parameter#
-ivy.size(x)[source]#
+ivy.size(x)[source]#

Return the number of elements of the array x.

Parameters:
@@ -4031,7 +4031,7 @@

Parameter#
-ivy.strides(x, /)[source]#
+ivy.strides(x, /)[source]#

Return the input array’s strides across each dimension.

Parameters:
@@ -4417,7 +4417,7 @@

Parameter#
-ivy.unset_inplace_mode()[source]#
+ivy.unset_inplace_mode()[source]#

Reset the memory management behavior for in-place updates in Ivy to the previous state.

@@ -4558,7 +4558,7 @@

Parameter#
-ivy.unset_shape_array_mode()[source]#
+ivy.unset_shape_array_mode()[source]#

Reset the mode of returning shape as ivy.Array to the previous state.

Return type:
@@ -4678,7 +4678,7 @@

Parameter#
-ivy.vmap(func, in_axes=0, out_axes=0)[source]#
+ivy.vmap(func, in_axes=0, out_axes=0)[source]#

Vectorizing map. Creates a function which maps func over argument axes.

Parameters:
diff --git a/docs/functional/ivy/ivy.functional.ivy.meta.html b/docs/functional/ivy/ivy.functional.ivy.meta.html index 7cdb97c1..f5f12607 100644 --- a/docs/functional/ivy/ivy.functional.ivy.meta.html +++ b/docs/functional/ivy/ivy.functional.ivy.meta.html @@ -1422,7 +1422,7 @@

Meta#

variables (Container) – Variables to be optimized during the meta step

  • inner_grad_steps (int) – Number of gradient steps to perform during the inner loop.

  • inner_learning_rate (float) – The learning rate of the inner loop.

    • -

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7f302ad552d0>) – The function used for the inner loop optimization. +

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7f41686612d0>) – The function used for the inner loop optimization. Default is ivy.gradient_descent_update.

  • inner_batch_fn (Optional[Callable], default: None) – Function to apply to the task sub-batch, before passing to the inner_cost_fn. Default is None.

    • @@ -1476,7 +1476,7 @@

    Meta#

    variables (Container) – Variables to be optimized during the meta step

  • inner_grad_steps (int) – Number of gradient steps to perform during the inner loop.

  • inner_learning_rate (float) – The learning rate of the inner loop.

    • -

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7f302ad552d0>) – The function used for the inner loop optimization. +

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7f41686612d0>) – The function used for the inner loop optimization. Default is ivy.gradient_descent_update.

  • inner_batch_fn (Optional[Callable], default: None) – Function to apply to the task sub-batch, before passing to the inner_cost_fn. Default is None.

    • @@ -1553,7 +1553,7 @@

    Meta#

    variables (Container) – Variables to be optimized.

  • inner_grad_steps (int) – Number of gradient steps to perform during the inner loop.

  • inner_learning_rate (float) – The learning rate of the inner loop.

    • -

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7f302ad552d0>) – The function used for the inner loop optimization. It takes the learnable +

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7f41686612d0>) – The function used for the inner loop optimization. It takes the learnable weights,the derivative of the cost with respect to the weights, and the learning rate as arguments, and returns the updated variables. Default is gradient_descent_update.

    • diff --git a/docs/functional/ivy/meta/ivy.functional.ivy.meta.fomaml_step.html b/docs/functional/ivy/meta/ivy.functional.ivy.meta.fomaml_step.html index c6bd2765..2cd2f371 100644 --- a/docs/functional/ivy/meta/ivy.functional.ivy.meta.fomaml_step.html +++ b/docs/functional/ivy/meta/ivy.functional.ivy.meta.fomaml_step.html @@ -1425,7 +1425,7 @@

    fomaml_stepContainer) – Variables to be optimized during the meta step

  • inner_grad_steps (int) – Number of gradient steps to perform during the inner loop.

  • inner_learning_rate (float) – The learning rate of the inner loop.

    • -

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7f302ad552d0>) – The function used for the inner loop optimization. +

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7f41686612d0>) – The function used for the inner loop optimization. Default is ivy.gradient_descent_update.

  • inner_batch_fn (Optional[Callable], default: None) – Function to apply to the task sub-batch, before passing to the inner_cost_fn. Default is None.

    • diff --git a/docs/functional/ivy/meta/ivy.functional.ivy.meta.maml_step.html b/docs/functional/ivy/meta/ivy.functional.ivy.meta.maml_step.html index c487e098..6671a8e0 100644 --- a/docs/functional/ivy/meta/ivy.functional.ivy.meta.maml_step.html +++ b/docs/functional/ivy/meta/ivy.functional.ivy.meta.maml_step.html @@ -1425,7 +1425,7 @@

    maml_stepContainer) – Variables to be optimized during the meta step

  • inner_grad_steps (int) – Number of gradient steps to perform during the inner loop.

  • inner_learning_rate (float) – The learning rate of the inner loop.

    • -

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7f302ad552d0>) – The function used for the inner loop optimization. +

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7f41686612d0>) – The function used for the inner loop optimization. Default is ivy.gradient_descent_update.

  • inner_batch_fn (Optional[Callable], default: None) – Function to apply to the task sub-batch, before passing to the inner_cost_fn. Default is None.

    • diff --git a/docs/functional/ivy/meta/ivy.functional.ivy.meta.reptile_step.html b/docs/functional/ivy/meta/ivy.functional.ivy.meta.reptile_step.html index 8499464e..ce7235c8 100644 --- a/docs/functional/ivy/meta/ivy.functional.ivy.meta.reptile_step.html +++ b/docs/functional/ivy/meta/ivy.functional.ivy.meta.reptile_step.html @@ -1422,7 +1422,7 @@

    reptile_stepContainer) – Variables to be optimized.

  • inner_grad_steps (int) – Number of gradient steps to perform during the inner loop.

  • inner_learning_rate (float) – The learning rate of the inner loop.

    • -

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7f302ad552d0>) – The function used for the inner loop optimization. It takes the learnable +

  • inner_optimization_step (Callable, default: <function gradient_descent_update at 0x7f41686612d0>) – The function used for the inner loop optimization. It takes the learnable weights,the derivative of the cost with respect to the weights, and the learning rate as arguments, and returns the updated variables. Default is gradient_descent_update.

    • diff --git a/docs/helpers/ivy_tests.test_ivy.helpers.globals.html b/docs/helpers/ivy_tests.test_ivy.helpers.globals.html index e7caa148..d94a47de 100644 --- a/docs/helpers/ivy_tests.test_ivy.helpers.globals.html +++ b/docs/helpers/ivy_tests.test_ivy.helpers.globals.html @@ -1411,7 +1411,7 @@

    Should not be used inside any of the test functions.

    -ivy_tests.test_ivy.helpers.globals.CURRENT_FRONTEND_CONFIG: <object object at 0x7f301eb25f60>#
    +ivy_tests.test_ivy.helpers.globals.CURRENT_FRONTEND_CONFIG: <object object at 0x7f415c425fb0>#
    diff --git a/docs/stateful/ivy.stateful.layers.html b/docs/stateful/ivy.stateful.layers.html index 16bf658a..eaf1429f 100644 --- a/docs/stateful/ivy.stateful.layers.html +++ b/docs/stateful/ivy.stateful.layers.html @@ -1538,8 +1538,8 @@

  • strides – The stride of the sliding window for each dimension of input.

  • padding – SAME” or “VALID” indicating the algorithm, or list indicating the per-dimension paddings.

    • -

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f302a971990>) – Initializer for the weights. Default is GlorotUniform.

    • -

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f302a971930>) – Initializer for the bias. Default is Zeros.

    • +

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f416828f5e0>) – Initializer for the weights. Default is GlorotUniform.

    • +

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f416828f580>) – Initializer for the bias. Default is Zeros.

  • with_bias (default: True) – Whether or not to include a bias term, default is True.

  • data_format (default: 'NWC') – NWC” or “NCW”. Defaults to “NWC”.

  • dilations (default: 1) – The dilation factor for each dimension of input. (Default value = 1)

    • @@ -1576,8 +1576,8 @@

  • strides – The stride of the sliding window for each dimension of input.

  • padding – SAME” or “VALID” indicating the algorithm, or list indicating the per-dimension paddings.

    • -

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f302a9718d0>) – Initializer for the weights. Default is GlorotUniform.

    • -

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f302a971870>) – Initializer for the bias. Default is Zeros.

    • +

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f416828f520>) – Initializer for the weights. Default is GlorotUniform.

    • +

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f416828f4c0>) – Initializer for the bias. Default is Zeros.

  • with_bias (default: True) – Whether or not to include a bias term, default is True.

  • output_shape (default: None) – Shape of the output (Default value = None)

  • data_format (default: 'NWC') – NWC” or “NCW”. Defaults to “NWC”.

    • @@ -1615,8 +1615,8 @@

  • strides – The stride of the sliding window for each dimension of input.

  • padding – SAME” or “VALID” indicating the algorithm, or list indicating the per-dimension paddings.

    • -

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f302a971810>) – Initializer for the weights. Default is GlorotUniform.

    • -

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f302a9717b0>) – Initializer for the bias. Default is Zeros.

    • +

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f416828f460>) – Initializer for the weights. Default is GlorotUniform.

    • +

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f416828f400>) – Initializer for the bias. Default is Zeros.

  • with_bias (default: True) – Whether or not to include a bias term, default is True.

  • data_format (default: 'NHWC') – NHWC” or “NCHW”. Defaults to “NHWC”.

  • dilations (default: 1) – The dilation factor for each dimension of input. (Default value = 1)

    • @@ -1653,8 +1653,8 @@

  • strides – The stride of the sliding window for each dimension of input.

  • padding – SAME” or “VALID” indicating the algorithm, or list indicating the per-dimension paddings.

    • -

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f302a971750>) – Initializer for the weights. Default is GlorotUniform.

    • -

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f302a9716f0>) – Initializer for the bias. Default is Zeros.

    • +

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f416828f3a0>) – Initializer for the weights. Default is GlorotUniform.

    • +

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f416828f340>) – Initializer for the bias. Default is Zeros.

  • with_bias (default: True) – Whether or not to include a bias term, default is True.

  • output_shape (default: None) – Shape of the output (Default value = None)

  • data_format (default: 'NHWC') – NHWC” or “NCHW”. Defaults to “NHWC”.

    • @@ -1692,8 +1692,8 @@

  • strides – The stride of the sliding window for each dimension of input.

  • padding – SAME” or “VALID” indicating the algorithm, or list indicating the per-dimension paddings.

    • -

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f302a9715d0>) – Initializer for the weights. Default is GlorotUniform.

    • -

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f302a971570>) – Initializer for the bias. Default is Zeros.

    • +

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f416828f220>) – Initializer for the weights. Default is GlorotUniform.

    • +

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f416828f1c0>) – Initializer for the bias. Default is Zeros.

  • with_bias (default: True) – Whether or not to include a bias term, default is True.

  • data_format (default: 'NDHWC') – NDHWC” or “NCDHW”. Defaults to “NDHWC”.

  • dilations (default: 1) – The dilation factor for each dimension of input. (Default value = 1)

    • @@ -1730,8 +1730,8 @@

  • strides – The stride of the sliding window for each dimension of input.

  • padding – SAME” or “VALID” indicating the algorithm, or list indicating the per-dimension paddings.

    • -

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f302a971510>) – Initializer for the weights. Default is GlorotUniform.

    • -

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f302a9714b0>) – Initializer for the bias. Default is Zeros.

    • +

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f416828f160>) – Initializer for the weights. Default is GlorotUniform.

    • +

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f416828f100>) – Initializer for the bias. Default is Zeros.

  • with_bias (default: True) – Whether or not to include a bias term, default is True.

  • output_shape (default: None) – Shape of the output (Default value = None)

  • data_format (default: 'NDHWC') – NDHWC” or “NCDHW”. Defaults to “NDHWC”.

    • @@ -1794,8 +1794,8 @@

  • strides – The stride of the sliding window for each dimension of input.

  • padding – SAME” or “VALID” indicating the algorithm, or list indicating the per-dimension paddings.

    • -

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f302a971690>) – Initializer for the weights. Default is GlorotUniform.

    • -

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f302a971630>) – Initializer for the bias. Default is Zeros.

    • +

  • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f416828f2e0>) – Initializer for the weights. Default is GlorotUniform.

    • +

  • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f416828f280>) – Initializer for the bias. Default is Zeros.

  • with_bias (default: True) – Whether or not to include a bias term, default is True.

  • data_format (default: 'NHWC') – NHWC” or “NCHW”. Defaults to “NHWC”.

  • dilations (default: 1) – The dilation factor for each dimension of input. (Default value = 1)

    • @@ -1951,7 +1951,7 @@

    • input_channels – Number of input channels for the layer

    • output_channels – Number of output channels for the layer

      • -

    • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f302a971450>) – Initializer for the weights. Default is GlorotUniform.

      • +

    • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f416828f0a0>) – Initializer for the weights. Default is GlorotUniform.

    • num_layers (default: 1) – Number of lstm cells in the lstm layer, default is 1.

    • return_sequence (default: True) – Whether or not to return the entire output sequence, or just the latest timestep. @@ -2010,8 +2010,8 @@

      • input_channels – Number of input channels for the layer.

      • output_channels – Number of output channels for the layer.

        • -

      • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f302a971a50>) – Initializer for the weights. Default is GlorotUniform.

        • -

      • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f302a9719f0>) – Initializer for the bias. Default is Zeros.

        • +

      • weight_initializer (default: <ivy.stateful.initializers.GlorotUniform object at 0x7f416828f6a0>) – Initializer for the weights. Default is GlorotUniform.

        • +

      • bias_initializer (default: <ivy.stateful.initializers.Zeros object at 0x7f416828f640>) – Initializer for the bias. Default is Zeros.

      • with_bias (default: True) – Whether or not to include a bias term, default is True.

      • device (default: None) – device on which to create the layer’s variables ‘cuda:0’, ‘cuda:1’, ‘cpu’ etc. Default is cpu.

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30, 33, 46, 48, 50, 51], "manual": [2, 6, 7, 13, 14, 15, 17, 19, 23, 24, 25, 26, 27, 28, 29, 30, 33, 642, 719, 729, 730, 820, 821, 822, 831, 837, 846, 855, 858], "mind": [2, 17, 19, 23, 29, 32, 36, 820, 821, 826, 829, 846, 858, 866], "click": [2, 4, 48, 820, 821, 822, 830, 834, 836, 837, 852], "runtim": [2, 4, 5, 8, 11, 12, 13, 14, 25, 32, 35, 46, 47, 824, 839, 846, 849, 872], "restart": [2, 4, 5, 8, 12, 13, 46, 47, 821, 836], "git": [2, 4, 5, 8, 12, 32, 46, 47, 48, 49, 814, 816, 819, 821, 822, 825, 828, 830, 836, 837, 846, 858], "clone": [2, 4, 8, 12, 32, 46, 48, 49, 814, 816, 822, 836, 858], "http": [2, 4, 5, 6, 7, 8, 11, 12, 13, 14, 19, 27, 28, 29, 30, 32, 33, 46, 47, 48, 49, 50, 51, 57, 58, 80, 81, 83, 148, 156, 244, 254, 255, 270, 329, 336, 337, 370, 373, 376, 379, 388, 420, 493, 523, 616, 617, 630, 631, 633, 636, 638, 640, 648, 686, 687, 715, 765, 814, 816, 821, 822, 825, 828, 830, 831, 834, 836, 858, 866], "github": [2, 4, 5, 8, 11, 12, 14, 32, 46, 47, 48, 49, 50, 814, 816, 817, 819, 822, 823, 825, 828, 830, 831, 833, 834, 836, 837, 845, 846, 858, 861, 880], "com": [2, 4, 5, 6, 7, 8, 11, 12, 14, 19, 32, 46, 47, 48, 49, 50, 814, 816, 821, 822, 825, 828, 830, 831, 836, 858], "unifyai": [2, 4, 8, 12, 32, 46, 47, 48, 49, 50, 814, 816, 821, 822, 828, 836, 858], "model": [2, 3, 4, 9, 15, 16, 21, 22, 23, 49, 51, 241, 274, 378, 454, 633, 790, 794, 795, 812, 854, 855, 859, 865, 866, 870, 871, 872, 873, 874, 875, 876, 878, 879], "depth": [2, 4, 6, 8, 12, 47, 54, 58, 62, 77, 81, 85, 142, 376, 379, 412, 472, 546, 558, 630, 635, 637, 655, 656, 822, 830, 854, 855, 856, 858], "repositori": [2, 4, 8, 12, 816, 820, 821, 822, 824, 825, 828, 836, 845, 863], "cd": [2, 4, 8, 12, 32, 49, 814, 816, 821, 822, 836, 858], "resnet": [3, 6, 14, 21, 32, 865, 866], "imag": [3, 4, 6, 7, 11, 14, 17, 21, 29, 32, 33, 46, 47, 48, 49, 50, 51, 58, 62, 80, 81, 85, 103, 221, 222, 223, 224, 227, 230, 239, 242, 244, 246, 255, 256, 257, 262, 264, 277, 284, 285, 287, 288, 292, 376, 395, 396, 412, 413, 414, 416, 546, 633, 635, 637, 650, 651, 652, 653, 654, 657, 658, 659, 793, 814, 821, 836, 849, 851, 852, 854, 856, 858, 865, 866, 872], "classif": [3, 4, 12, 15, 21, 46, 872], "acceler": [3, 21, 831, 843, 870, 874, 875, 876, 877], "convert": [3, 8, 9, 11, 14, 15, 17, 19, 21, 22, 24, 26, 29, 30, 32, 33, 34, 36, 38, 46, 49, 51, 53, 54, 57, 75, 76, 77, 80, 98, 128, 129, 141, 151, 152, 194, 195, 196, 197, 208, 216, 220, 240, 280, 379, 384, 463, 464, 465, 514, 579, 597, 599, 600, 601, 603, 630, 631, 632, 633, 635, 638, 642, 696, 720, 731, 732, 774, 802, 807, 820, 826, 827, 840, 841, 843, 846, 848, 851, 857, 859, 863, 866, 870, 871, 878], "faster": [3, 4, 9, 11, 14, 15, 21, 32, 33, 49, 51, 58, 63, 81, 86, 377, 450, 638, 688, 816, 819, 828, 859, 874, 877], "infer": [3, 6, 7, 9, 11, 13, 14, 15, 21, 25, 35, 37, 38, 47, 49, 51, 54, 58, 59, 62, 65, 77, 81, 82, 85, 88, 127, 129, 132, 136, 137, 141, 144, 150, 159, 160, 161, 162, 163, 313, 314, 376, 379, 383, 412, 497, 511, 557, 591, 592, 630, 631, 635, 637, 640, 660, 707, 802, 803, 824, 827, 831, 832, 846, 851, 856, 866, 870, 871, 874, 876], "mmpretrain": [3, 21], "segment": [3, 21, 58, 81, 331, 332, 333, 370, 828, 833], "unet": [3, 21], "alexnet": [3, 21], "written": [3, 4, 5, 6, 13, 21, 23, 32, 33, 46, 59, 379, 474, 821, 825, 826, 834, 837, 838, 842, 843, 847, 851, 853, 856, 857, 861, 866, 870, 872, 876, 878, 879], "xgboost": [3, 21], "paddlepaddl": [3, 21, 336, 337, 373, 821], "dinov2": [3, 7, 21], "project": [3, 12, 14, 21, 26, 27, 28, 29, 30, 32, 33, 36, 99, 637, 664, 793, 814, 816, 817, 820, 821, 822, 823, 826, 827, 828, 846, 855, 857, 861, 862, 863, 866, 868, 870, 872, 875, 879, 880], "convnext": [3, 6, 11, 13, 21], "finetun": [3, 21, 46], "video": [4, 8, 11, 12, 14, 17, 19, 23, 24, 25, 26, 27, 28, 29, 30, 33, 814, 815, 820, 821, 822, 825, 826, 827, 829, 830, 831, 832, 833, 834, 835, 837, 838, 839, 840, 841, 842, 843, 844, 846, 847, 849, 858, 870], "tutori": [4, 6, 7, 8, 11, 12, 13, 14, 17, 19, 23, 24, 25, 26, 27, 28, 29, 30, 33, 814, 822, 843, 858], "three": [4, 5, 21, 27, 37, 38, 48, 58, 140, 313, 370, 379, 465, 630, 821, 822, 829, 830, 831, 833, 843, 846, 849, 850, 851, 873, 878], "major": [4, 5, 645, 748, 831, 832, 844, 846, 857, 862, 869, 872], "ml": [4, 5, 6, 13, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 32, 33, 34, 35, 36, 37, 38, 39, 46, 48, 51, 815, 819, 843, 850, 851, 852, 854, 855, 856, 860, 862, 863, 866, 868, 869, 870, 871, 872, 875, 877, 879], "framework": [4, 5, 7, 9, 17, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 33, 34, 35, 36, 37, 39, 46, 48, 50, 53, 59, 171, 193, 203, 206, 217, 544, 560, 564, 596, 599, 631, 632, 635, 642, 721, 772, 774, 778, 785, 790, 797, 802, 803, 817, 818, 820, 821, 824, 825, 826, 827, 828, 830, 831, 832, 833, 835, 836, 838, 839, 840, 842, 843, 846, 847, 849, 850, 851, 853, 856, 857, 858, 859, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 873, 876], "sinc": [4, 8, 12, 13, 29, 30, 32, 33, 46, 48, 58, 81, 99, 373, 816, 821, 822, 825, 826, 827, 828, 829, 830, 831, 832, 835, 842, 843, 857, 862, 872, 878], "automat": [4, 8, 9, 12, 13, 30, 32, 33, 38, 820, 821, 822, 824, 827, 828, 830, 831, 837, 839, 842, 846, 849, 850, 852, 855, 856, 858, 859, 863, 872, 875, 879], "sure": [4, 8, 11, 12, 13, 14, 15, 32, 46, 817, 820, 821, 822, 825, 830, 835, 836, 843, 844, 846, 849, 858], "enabl": [4, 5, 6, 8, 11, 12, 13, 14, 15, 27, 28, 30, 47, 58, 63, 75, 86, 104, 376, 378, 399, 457, 581, 635, 638, 681, 795, 812, 814, 821, 822, 823, 826, 829, 831, 839, 840, 841, 842, 843, 846, 847, 850, 852, 854, 856, 857, 859, 862, 865, 870, 871, 872, 873, 874, 875, 878, 879], "dm": [4, 5, 8, 11, 14, 32, 33, 44, 46], "haiku": [4, 5, 8, 11, 14, 30, 32, 33, 44, 46, 50, 790, 814, 856, 863, 866, 872], "exit": [4, 8, 12, 13, 32, 33, 832], "download": [4, 6, 7, 12, 13, 17, 19, 32, 33, 47, 48, 51, 816, 821, 828, 846, 865, 866], "imagenet": [4, 6, 13, 19, 47, 49, 814], "class": [4, 6, 7, 8, 12, 13, 15, 17, 19, 23, 32, 33, 44, 45, 46, 47, 48, 49, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 106, 107, 108, 135, 144, 150, 166, 169, 182, 184, 185, 244, 281, 339, 361, 373, 387, 388, 396, 397, 430, 529, 530, 537, 546, 550, 563, 573, 596, 630, 631, 632, 633, 635, 637, 638, 639, 642, 643, 658, 663, 667, 673, 683, 687, 688, 690, 697, 713, 720, 731, 738, 753, 760, 764, 765, 774, 775, 782, 783, 784, 785, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 801, 802, 805, 807, 812, 814, 820, 827, 828, 829, 831, 832, 833, 834, 838, 840, 841, 844, 845, 846, 849, 851, 852, 854, 855, 856, 859, 865, 866, 870, 872, 873, 879], "wget": [4, 6, 8, 12, 46, 47, 50, 821], "raw": [4, 6, 7, 8, 11, 12, 14, 29, 32, 33, 46, 49, 50, 75, 814, 834, 866, 873], "githubusercont": [4, 6, 8, 12, 46, 50], "hub": [4, 6, 8, 12, 46, 49, 51], "master": [4, 8, 12, 24, 25, 26, 34, 35, 36, 37, 38, 39, 46, 48, 49, 50, 817, 830, 872, 880], "imagenet_class": [4, 12], "categori": [4, 6, 12, 820, 825, 826, 829, 831, 835, 843, 847, 850], "strip": [4, 12, 25, 35, 862], "readlin": [4, 12, 47], "cat": [4, 7, 12, 47, 844, 849, 851, 856, 865, 866], "jpg": [4, 6, 7, 8, 11, 12, 14, 29, 32, 33, 48, 49, 814, 866], "filenam": [4, 8, 12, 13, 32, 33, 46, 48, 51, 59, 795, 801, 854], "import": [4, 6, 7, 9, 10, 11, 13, 14, 17, 19, 24, 25, 26, 27, 28, 29, 30, 32, 33, 34, 35, 36, 37, 38, 39, 46, 47, 49, 50, 51, 58, 69, 73, 77, 81, 96, 195, 196, 200, 212, 308, 388, 523, 558, 574, 632, 635, 641, 646, 717, 718, 753, 785, 802, 803, 814, 819, 820, 821, 822, 823, 825, 826, 827, 828, 829, 831, 832, 833, 834, 837, 840, 841, 842, 843, 844, 845, 846, 847, 851, 853, 854, 856, 857, 858, 862, 865, 866, 867, 868, 870, 872, 875, 876, 878], "devic": [4, 6, 7, 8, 9, 11, 12, 13, 14, 47, 48, 51, 54, 58, 67, 75, 77, 81, 90, 103, 106, 107, 108, 127, 128, 129, 131, 132, 133, 136, 137, 138, 139, 141, 142, 143, 144, 146, 147, 148, 149, 150, 194, 195, 196, 197, 198, 199, 200, 201, 202, 207, 208, 209, 210, 212, 213, 214, 215, 216, 218, 220, 313, 314, 329, 330, 370, 383, 473, 509, 510, 512, 513, 537, 551, 552, 630, 635, 644, 739, 740, 741, 742, 772, 774, 775, 790, 792, 793, 794, 795, 796, 797, 798, 799, 812, 822, 824, 827, 831, 835, 839, 840, 844, 846, 847, 849, 851, 856, 857, 858, 859, 862, 871, 872, 874, 875, 876, 877], "torchvis": [4, 6, 11, 12, 13, 46, 863], "transform": [4, 5, 6, 7, 11, 12, 13, 14, 29, 32, 33, 46, 47, 49, 58, 62, 81, 85, 376, 377, 398, 399, 404, 405, 408, 409, 410, 420, 421, 424, 441, 637, 661, 777, 780, 793, 814, 840, 846, 856, 859, 865, 866, 870, 872, 873, 874], "pil": [4, 6, 7, 8, 11, 12, 14, 29, 32, 33, 47, 48, 49, 814, 866], "time": [4, 5, 6, 7, 9, 10, 11, 13, 14, 30, 32, 33, 38, 46, 48, 49, 50, 58, 60, 63, 69, 81, 83, 92, 98, 99, 135, 342, 373, 376, 377, 379, 388, 405, 410, 422, 424, 445, 452, 485, 491, 523, 617, 622, 630, 636, 637, 638, 640, 641, 645, 646, 660, 663, 678, 713, 716, 717, 718, 745, 746, 750, 751, 793, 794, 795, 812, 820, 821, 822, 825, 827, 829, 830, 831, 833, 836, 838, 839, 840, 842, 843, 846, 847, 851, 854, 856, 857, 858, 861, 862, 863, 865, 866, 870, 872, 873, 876, 877, 878], "filterwarn": [4, 5, 13], "ignor": [4, 5, 13, 45, 53, 54, 58, 75, 81, 140, 376, 377, 379, 388, 400, 401, 402, 431, 439, 447, 487, 488, 492, 531, 630, 637, 642, 664, 730, 731, 797, 821, 828, 830, 833, 846, 857, 878], "compos": [4, 6, 7, 11, 12, 13, 32, 33, 46, 58, 81, 376, 390, 391, 392, 393, 821, 829, 843, 846, 865, 867, 872, 879], "resiz": [4, 6, 7, 8, 11, 12, 13, 46, 47, 58, 81, 376, 412, 849], "centercrop": [4, 12, 13], "224": [4, 6, 7, 12, 13, 17, 19, 32, 33, 46, 47, 49, 814, 866], "totensor": [4, 6, 7, 11, 12, 13, 46], "485": [4, 12, 13, 46], "456": [4, 12, 13, 46, 846], "406": [4, 12, 13, 46, 58, 81, 398, 541, 635], "229": [4, 12, 13, 46, 280, 633], "225": [4, 12, 13, 46, 48, 235, 633], "torch_img": [4, 8, 12], "unsqueez": [4, 8, 11, 12], "img": [4, 8, 12, 29, 32, 33, 46, 47, 48, 50, 814, 854, 866], "ipython": [4, 8, 12, 27, 28, 29, 30, 32, 33, 51], "displai": [4, 8, 12, 13, 29, 32, 33, 46, 47, 48, 50, 51, 821, 828, 830, 835, 846, 854], "end": [4, 8, 13, 46, 47, 58, 81, 127, 229, 285, 354, 373, 376, 378, 379, 424, 453, 475, 485, 487, 488, 630, 633, 808, 821, 822, 827, 830, 836, 842, 847, 849, 850, 857, 870, 875], "set_default_devic": [4, 5, 6, 8, 11, 12, 13, 14, 218, 632, 832], "ivy_model": [4, 5, 8, 12, 49], "ivy_alexnet": 4, "quick": [4, 21, 33, 822, 824, 844, 855], "trace_graph": [4, 5, 8, 12, 25, 26, 27, 28, 32, 33, 35, 36, 37, 38, 39, 40, 49, 795, 814, 851, 856, 864], "moment": [4, 58, 60, 81, 83, 377, 434, 616, 617, 622, 636, 797, 812, 820, 827, 857, 865, 866], "cost": [4, 60, 83, 616, 617, 620, 622, 623, 624, 636, 641, 716, 717, 718, 808, 831, 849, 870], "arg": [4, 6, 8, 9, 10, 11, 12, 13, 17, 19, 27, 28, 30, 32, 33, 37, 38, 39, 50, 53, 75, 97, 107, 123, 204, 214, 602, 629, 630, 632, 635, 772, 774, 789, 790, 793, 794, 795, 799, 802, 807, 812, 814, 826, 831, 832, 835, 841, 842, 843, 849, 851, 855, 865, 866, 867], "asarrai": [4, 5, 8, 11, 12, 47, 54, 58, 59, 70, 77, 81, 82, 93, 128, 386, 515, 516, 546, 557, 561, 562, 592, 593, 594, 630, 635, 637, 646, 647, 651, 751, 755, 835, 840, 843, 844], "cuda": [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 23, 32, 47, 48, 51, 54, 58, 67, 77, 81, 90, 138, 139, 142, 194, 195, 196, 212, 383, 509, 510, 512, 513, 630, 632, 638, 644, 689, 739, 740, 741, 742, 792, 793, 794, 795, 796, 797, 798, 812, 851, 857, 859, 877], "output": [4, 5, 7, 8, 9, 10, 12, 13, 23, 29, 30, 32, 33, 45, 46, 47, 49, 52, 54, 55, 56, 57, 58, 59, 60, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 74, 76, 77, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 89, 90, 91, 93, 94, 95, 103, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 127, 128, 129, 130, 131, 132, 133, 134, 136, 137, 138, 139, 140, 142, 143, 144, 145, 146, 147, 149, 150, 153, 155, 180, 214, 215, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 318, 319, 323, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 365, 366, 367, 368, 370, 373, 375, 376, 377, 378, 379, 382, 383, 384, 386, 388, 389, 390, 391, 392, 393, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 408, 409, 410, 412, 413, 414, 415, 418, 420, 421, 422, 424, 425, 427, 428, 429, 431, 433, 436, 437, 439, 442, 443, 444, 445, 447, 448, 451, 453, 454, 455, 456, 457, 458, 459, 460, 461, 468, 469, 470, 473, 475, 476, 477, 478, 479, 482, 483, 484, 486, 487, 488, 489, 490, 491, 492, 493, 494, 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44, 802, 844], "deepmind": [18, 863], "perceiverio": [18, 863], "backbon": [18, 46, 814, 851, 854], "TO": [18, 20, 31], "efficientnet": 19, "eff_encod": [19, 814], "efficientnet_v2": [19, 814], "efficientnetv2b0": [19, 814], "storag": [19, 46, 47, 854, 862], "googleapi": [19, 46, 47], "efficientnetv2": 19, "b0_notop": 19, "h5": [19, 75], "24274472": 19, "0u": 19, "torch_eff_encod": [19, 814], "modes_to_trac": 19, "1280": [19, 546, 635, 814], "welcom": [21, 47, 814, 815, 821, 822, 823, 845], "varieti": [21, 825, 830, 831, 832, 846, 848, 868, 870, 874, 875, 878, 879], "organ": [21, 826, 829, 839, 843, 845, 847, 859, 862], "main": [21, 33, 54, 58, 63, 81, 86, 133, 146, 147, 148, 314, 329, 330, 370, 377, 379, 428, 474, 630, 638, 671, 672, 692, 814, 817, 820, 821, 822, 823, 825, 828, 829, 836, 840, 842, 870, 872, 873, 878], "exactli": [21, 25, 35, 44, 45, 49, 291, 633, 820, 829, 830, 831, 832, 833, 835, 846, 849, 861, 863], "rush": [21, 863], "jump": [21, 844], "straight": [21, 814, 830, 843, 846, 853], "quickstart": [21, 814], "introduct": [21, 23, 30, 32, 33, 872], "point": [21, 30, 55, 57, 58, 63, 67, 69, 71, 78, 80, 81, 86, 90, 94, 127, 128, 129, 131, 133, 136, 143, 144, 149, 153, 166, 170, 174, 181, 221, 222, 223, 224, 226, 227, 228, 229, 230, 237, 238, 239, 241, 242, 244, 246, 247, 248, 254, 255, 256, 257, 262, 263, 264, 265, 266, 274, 276, 277, 279, 281, 283, 284, 285, 286, 287, 288, 289, 291, 292, 293, 294, 295, 313, 314, 316, 336, 337, 354, 355, 358, 360, 370, 373, 376, 377, 378, 383, 388, 391, 400, 401, 402, 420, 430, 450, 454, 509, 510, 511, 512, 513, 523, 524, 525, 533, 628, 630, 631, 633, 638, 644, 645, 646, 647, 648, 668, 670, 673, 674, 675, 677, 679, 680, 681, 684, 685, 686, 687, 688, 689, 690, 692, 695, 741, 742, 748, 750, 751, 752, 753, 756, 758, 759, 761, 762, 763, 764, 765, 766, 767, 802, 803, 812, 818, 820, 821, 822, 825, 826, 828, 830, 831, 833, 834, 836, 838, 842, 843, 846, 847, 849, 851, 853, 854, 863, 865, 878], "showcas": [21, 814], "real": 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81, 85, 151, 152, 164, 171, 193, 194, 195, 196, 197, 199, 208, 215, 216, 220, 376, 377, 379, 419, 423, 431, 485, 496, 525, 544, 631, 632, 635, 637, 638, 650, 651, 652, 653, 655, 657, 659, 675, 772, 774, 778, 807, 808, 827, 828, 830, 831, 832, 835, 843, 851, 854], "simplest": [23, 821, 833, 846, 849], "interact": [23, 32, 47, 50, 820, 871, 872, 877], "submodul": [23, 32, 46, 48, 103, 104, 627, 628, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 789, 790, 792, 793, 795, 796, 797, 798, 820, 821, 822, 825, 828, 830, 832, 836, 839, 840, 846, 850, 851, 855, 859], "likewis": [23, 28, 32, 39, 822, 829, 831, 834, 838, 839, 843, 849, 854, 865, 866, 878], "nativearrai": [23, 32, 33, 53, 54, 55, 57, 58, 59, 60, 62, 63, 64, 65, 66, 67, 69, 71, 74, 76, 77, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 103, 107, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 123, 124, 126, 128, 129, 130, 132, 137, 138, 139, 140, 141, 142, 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46, 48, 53, 75, 76, 167, 168, 200, 201, 377, 449, 551, 552, 558, 631, 632, 635, 642, 719, 720, 723, 729, 730, 731, 772, 821, 825, 828, 829, 836, 839, 842, 855, 857], "fashion": [23, 779, 846, 866], "native_arrai": [23, 32, 33, 54, 55, 57, 77, 79, 80, 81, 82, 86, 93, 111, 114, 137, 140, 142, 144, 150, 153, 154, 155, 156, 164, 169, 176, 198, 207, 215, 231, 235, 240, 241, 242, 244, 248, 252, 260, 261, 269, 274, 277, 280, 283, 288, 336, 337, 364, 373, 378, 379, 459, 485, 491, 495, 535, 538, 565, 566, 569, 600, 627, 630, 631, 632, 633, 635, 637, 638, 639, 640, 644, 645, 648, 649, 651, 652, 659, 667, 670, 674, 675, 680, 681, 685, 689, 690, 692, 695, 697, 699, 700, 707, 739, 748, 757, 763, 766, 768, 774, 784, 802, 818, 836, 844, 846], "data_class": [23, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 106, 107, 108, 396, 397, 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"x0": [32, 33, 51, 82, 538, 635, 833], "normalize_trac": [32, 33], "html": [32, 33, 47, 57, 58, 80, 81, 148, 156, 244, 254, 255, 270, 329, 336, 337, 370, 373, 376, 379, 388, 420, 493, 523, 630, 631, 633, 638, 640, 648, 686, 687, 715, 765, 834, 862], "fname": [32, 33, 49, 51, 795, 854], "anticip": [32, 33], "addition": [32, 33, 829, 842, 843, 878], "normalize_native_comp": [32, 33], "return_backend_compiled_fn": 32, "immedi": [32, 33, 812, 814, 820, 821], "built": [32, 33, 38, 46, 48, 51, 127, 630, 793, 794, 795, 821, 822, 828, 829, 846, 852, 858, 865, 871, 872, 876], "eager_graph": [32, 33, 814, 865, 866], "lazy_graph": [32, 33, 814, 865, 866], "thought": [32, 33, 821, 822, 838, 862, 870], "matter": [32, 33, 38, 833, 861], "haven": [32, 33, 38, 858, 872], "jax_out": [32, 33], "ideal": [32, 33, 830, 831, 843, 849, 854], "worth": [32, 33], "differenti": [32, 33, 296, 366, 367, 368, 375, 872], "chosen": [32, 33, 51, 101, 127, 229, 630, 633, 645, 749, 820, 830, 843], "plai": [32, 33, 378, 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336, 337, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 376, 379, 388, 395, 396, 397, 398, 400, 401, 402, 404, 408, 409, 410, 413, 414, 415, 419, 420, 423, 424, 425, 426, 427, 428, 430, 431, 432, 433, 434, 435, 437, 441, 442, 443, 444, 445, 446, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 469, 470, 471, 472, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 508, 510, 511, 512, 513, 514, 515, 516, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 538, 539, 541, 542, 545, 546, 547, 548, 549, 550, 553, 554, 557, 559, 561, 562, 563, 565, 566, 567, 569, 570, 572, 577, 578, 592, 593, 594, 595, 596, 598, 600, 601, 614, 616, 617, 620, 622, 623, 624, 625, 651, 652, 653, 654, 655, 656, 659, 660, 661, 663, 667, 668, 669, 671, 672, 673, 674, 675, 676, 677, 678, 679, 681, 684, 685, 686, 688, 692, 693, 695, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 738, 739, 740, 741, 742, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 767, 768, 769, 826, 833, 834, 849], "docstr": [52, 54, 55, 56, 57, 58, 59, 60, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 74, 76, 77, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 103, 111, 112, 113, 114, 115, 116, 117, 118, 119, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 149, 150, 154, 155, 156, 166, 169, 173, 174, 181, 198, 215, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 300, 301, 302, 304, 305, 306, 307, 308, 310, 311, 312, 313, 314, 315, 316, 318, 319, 320, 323, 330, 332, 333, 334, 335, 336, 337, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 373, 376, 379, 388, 395, 396, 397, 398, 400, 401, 402, 404, 408, 409, 410, 413, 414, 415, 419, 420, 423, 424, 425, 426, 427, 428, 430, 431, 432, 433, 434, 435, 437, 441, 442, 443, 444, 445, 446, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 469, 470, 471, 472, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 508, 510, 511, 512, 513, 514, 515, 516, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 538, 539, 541, 542, 545, 546, 547, 548, 549, 550, 553, 554, 557, 559, 561, 562, 563, 565, 566, 567, 569, 570, 572, 577, 578, 592, 593, 594, 595, 596, 598, 600, 601, 614, 615, 616, 617, 620, 622, 623, 624, 625, 630, 631, 633, 635, 638, 640, 645, 646, 647, 648, 649, 651, 652, 653, 654, 655, 656, 659, 660, 661, 663, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 694, 695, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 738, 739, 740, 741, 742, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 819, 820, 824, 828, 837, 838, 839, 840, 843, 845, 847], "liter": [52, 57, 58, 63, 74, 80, 81, 86, 111, 112, 113, 114, 115, 116, 117, 118, 119, 292, 296, 301, 302, 304, 368, 376, 377, 379, 382, 398, 408, 412, 420, 435, 441, 446, 449, 452, 485, 507, 627, 633, 638, 647, 679, 695, 756, 789, 849], "magnitud": [52, 57, 58, 74, 80, 81, 111, 112, 113, 114, 115, 116, 117, 118, 119, 221, 224, 241, 248, 274, 292, 296, 301, 302, 304, 368, 627, 633, 638, 688, 689, 789, 831], "handle_complex_input": [52, 57, 58, 74, 80, 81, 111, 112, 113, 114, 115, 116, 117, 118, 119, 292, 296, 301, 302, 304, 368, 627, 633, 789, 840], "element": [52, 54, 57, 58, 59, 62, 63, 65, 67, 68, 69, 71, 74, 75, 77, 78, 80, 81, 82, 85, 86, 88, 90, 91, 92, 94, 99, 103, 104, 107, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 127, 130, 136, 137, 146, 147, 148, 164, 166, 169, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 302, 304, 306, 307, 308, 310, 311, 312, 329, 330, 331, 332, 333, 335, 336, 337, 338, 339, 343, 346, 347, 348, 349, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 368, 370, 373, 376, 377, 378, 379, 388, 389, 400, 401, 402, 405, 410, 413, 414, 415, 419, 421, 422, 423, 429, 430, 431, 453, 463, 464, 465, 475, 476, 477, 479, 482, 492, 493, 495, 497, 499, 521, 522, 524, 525, 526, 527, 528, 529, 531, 532, 534, 538, 541, 542, 553, 554, 570, 572, 592, 593, 594, 596, 600, 601, 627, 630, 633, 635, 637, 638, 640, 642, 644, 645, 646, 647, 648, 649, 660, 669, 671, 673, 674, 678, 683, 685, 686, 688, 692, 700, 703, 704, 705, 706, 707, 708, 709, 710, 719, 722, 728, 739, 747, 748, 749, 750, 751, 752, 753, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 772, 774, 777, 779, 793, 808, 834, 844, 846, 849, 851, 876], "138": [52, 111, 627], "165": [52, 111, 627, 637, 661], "hardswish": [52, 58, 74, 81, 299, 368, 627, 789], "leaky_relu": [52, 74, 81, 296, 627, 778], "alpha": [52, 57, 58, 74, 80, 81, 108, 113, 224, 290, 296, 297, 305, 309, 315, 368, 370, 377, 382, 383, 431, 507, 510, 511, 512, 627, 633, 789, 838, 843, 844], "slope": [52, 58, 74, 81, 113, 296, 297, 303, 305, 309, 368, 627, 789], "leaki": [52, 74, 113, 627, 789], "log_softmax": [52, 74, 627, 789], "0719": [52, 74, 114], "mish": [52, 74, 627, 789], "30340147": [52, 115, 627], "86509842": [52, 74, 115, 627], "269": [52, 117], "881": [52, 57, 80, 117, 227, 240, 280, 633], "422": [52, 118, 627], "155": [52, 85, 118, 627, 637, 661], "softplu": [52, 74, 627, 789, 849], "beta": [52, 58, 66, 74, 81, 89, 119, 305, 309, 315, 318, 319, 368, 370, 377, 378, 382, 383, 431, 459, 507, 511, 512, 627, 643, 738, 789, 814, 849], "threshold": [52, 57, 58, 74, 80, 81, 119, 272, 273, 312, 338, 368, 373, 378, 379, 454, 459, 492, 627, 633, 789, 849], "union": [52, 53, 54, 55, 56, 57, 58, 59, 60, 62, 63, 64, 65, 66, 67, 68, 71, 72, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 123, 124, 126, 127, 128, 129, 130, 131, 132, 133, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 181, 182, 183, 184, 185, 186, 187, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 207, 208, 209, 210, 212, 213, 214, 215, 216, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 368, 370, 373, 374, 376, 377, 378, 379, 382, 383, 384, 386, 388, 390, 391, 392, 393, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 408, 409, 410, 412, 413, 414, 415, 416, 418, 419, 420, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 441, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 468, 469, 470, 471, 473, 474, 475, 476, 477, 478, 479, 480, 482, 483, 484, 485, 486, 487, 488, 489, 491, 492, 493, 494, 495, 497, 498, 499, 500, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 535, 538, 539, 541, 542, 546, 547, 548, 549, 550, 553, 554, 555, 556, 557, 559, 561, 562, 563, 565, 566, 569, 570, 572, 573, 577, 578, 582, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 614, 615, 616, 617, 618, 619, 620, 622, 623, 624, 625, 627, 629, 630, 631, 632, 633, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 663, 664, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 726, 727, 728, 730, 731, 736, 737, 738, 739, 740, 741, 742, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 774, 777, 792, 797, 798, 826, 829, 831, 832, 833, 835, 838, 839, 842, 847, 849, 851, 856, 865, 866, 867], "3461": [52, 74, 119, 627], "6491": [52, 74, 119, 627], "_array_to_new_backend": 53, "_to_ivi": 53, "_to_n": 53, "to_ignor": [53, 73, 96, 642, 730, 731], "_to_new_backend": 53, "args_to_ivi": 53, "include_deriv": [53, 76, 642, 720, 731, 774], "nest": [53, 75, 76, 104, 107, 244, 568, 598, 615, 618, 633, 635, 636, 641, 716, 717, 719, 720, 721, 722, 723, 724, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 797, 826, 828, 829, 839, 841, 847, 854, 855, 857, 859, 872], "unchang": [53, 57, 376, 379, 421, 475, 637, 660], "deriv": [53, 54, 58, 60, 76, 77, 81, 83, 132, 137, 144, 150, 314, 318, 343, 370, 373, 616, 617, 620, 621, 622, 623, 624, 630, 636, 641, 642, 718, 720, 731, 795, 797, 798, 831, 832, 853, 855], "word": [53, 127, 379, 478, 630, 644, 742, 790, 793, 829, 842, 843, 859], "args_to_n": [53, 842], "cont_inplac": 53, "decid": [53, 75, 642, 730, 731, 820, 821, 831, 849], "args_to_new_backend": 53, "shallow": [53, 642, 726, 727, 731, 736, 737], "nativevari": 53, "mutabl": [53, 642, 720, 726, 727, 731, 736, 737, 827], "to_ivi": [53, 76, 642, 732, 842], "leaf": [53, 75, 82, 94, 104, 549, 642, 729, 730, 732, 759, 829, 839, 854], "travers": [53, 76, 642, 723, 731, 829, 831, 835, 851], "lowest": [53, 58, 67, 76, 81, 90, 388, 526, 642, 644, 731, 740, 808, 839, 857, 859, 869, 873, 877], "search": [53, 58, 76, 81, 745, 746, 785, 819, 821, 829, 833, 836, 846, 847, 861], "to_new_backend": 53, "_arraywithcr": [54, 103], "boolean": [54, 55, 57, 58, 59, 65, 68, 71, 75, 77, 78, 80, 81, 82, 88, 91, 94, 103, 104, 124, 126, 128, 129, 130, 136, 153, 169, 171, 173, 174, 177, 193, 203, 211, 217, 231, 232, 233, 234, 235, 236, 268, 269, 270, 271, 336, 337, 352, 373, 377, 379, 435, 446, 452, 463, 464, 465, 471, 473, 475, 476, 477, 480, 484, 491, 493, 500, 535, 538, 549, 556, 559, 560, 564, 565, 566, 567, 568, 569, 570, 579, 582, 585, 586, 588, 589, 614, 629, 630, 631, 632, 633, 635, 637, 640, 641, 642, 645, 648, 664, 703, 704, 705, 707, 709, 710, 712, 714, 716, 717, 729, 747, 748, 749, 761, 763, 777, 778, 779, 780, 785, 796, 829, 831, 839, 843, 846, 849], "never": [54, 58, 65, 77, 81, 88, 129, 379, 463, 464, 465, 471, 473, 475, 476, 477, 480, 484, 491, 500, 556, 635, 640, 703, 704, 705, 707, 709, 710, 712, 714, 822, 831, 842, 843, 846], "valueerror": [54, 58, 65, 77, 81, 88, 92, 129, 376, 378, 410, 421, 458, 463, 464, 471, 473, 475, 476, 477, 484, 500, 640, 703, 704, 705, 707, 709, 710, 712, 714, 753, 779, 809, 835], "buffer": [54, 77, 81, 88, 129, 135, 463, 464, 471, 473, 475, 476, 477, 484, 500, 630, 703, 704, 705, 707, 709, 710, 712, 714, 794, 795, 842, 857], "nativedtyp": [54, 55, 58, 62, 63, 67, 68, 71, 77, 81, 86, 90, 91, 94, 127, 128, 129, 131, 132, 133, 135, 136, 137, 138, 139, 141, 142, 143, 144, 149, 150, 152, 153, 158, 159, 160, 161, 162, 163, 164, 165, 170, 171, 175, 177, 179, 183, 193, 313, 314, 315, 316, 317, 318, 319, 334, 341, 357, 370, 373, 383, 388, 509, 510, 511, 512, 513, 523, 524, 525, 526, 529, 532, 630, 631, 637, 638, 644, 645, 647, 648, 660, 679, 695, 740, 741, 742, 745, 746, 756, 758, 759, 762, 764, 766, 792, 831, 832, 838, 847, 851], "datatyp": [54, 58, 75, 77, 81, 129, 137, 141, 158, 179, 183, 376, 424, 630, 631, 772, 847, 865], "nativedevic": [54, 56, 58, 67, 77, 79, 81, 90, 127, 128, 129, 131, 132, 133, 136, 137, 138, 139, 141, 142, 143, 144, 148, 149, 150, 195, 196, 197, 198, 199, 202, 207, 208, 209, 210, 212, 213, 214, 215, 216, 220, 313, 314, 329, 370, 383, 509, 510, 512, 513, 630, 632, 644, 739, 740, 741, 742, 792, 797, 798, 831, 832, 835, 838, 847], "39999998": [54, 128, 129, 630, 646, 751], "5999999": [54, 58, 81, 85, 128, 129, 298, 368, 377, 426, 630, 637, 660, 667], "0999999": [54, 71, 128, 129, 298, 308, 311, 354, 368, 373, 630, 762], "10000038": [54, 128, 129, 630], "90786433e": [54, 128, 129, 630], "310": [54, 128, 129, 630], "copy_arrai": [54, 77, 630], "to_ivy_arrai": [54, 77, 130, 630], "empty_lik": [54, 58, 77, 81, 265, 377, 429, 630, 633], "uniniti": [54, 131, 132, 630, 837], "from_dlpack": [54, 77, 630], "full_lik": [54, 77, 630, 847], "fill_valu": [54, 58, 68, 77, 81, 91, 136, 137, 253, 261, 379, 383, 493, 513, 630, 633, 645, 748, 831, 844, 847], "scalar": [54, 57, 58, 59, 63, 74, 77, 80, 81, 82, 86, 98, 113, 137, 142, 224, 245, 290, 296, 339, 340, 342, 347, 350, 352, 354, 359, 373, 376, 377, 378, 379, 424, 431, 453, 463, 464, 465, 474, 479, 601, 614, 630, 633, 635, 638, 695, 831, 841, 843, 857, 872], "fill": [54, 57, 58, 67, 68, 75, 77, 80, 81, 90, 91, 131, 136, 137, 139, 142, 143, 144, 149, 150, 275, 314, 370, 377, 379, 383, 435, 441, 446, 452, 474, 493, 494, 510, 512, 513, 630, 633, 644, 645, 740, 748, 792, 820, 844], "000123": [54, 137, 630], "stop": [54, 58, 60, 77, 81, 83, 127, 138, 139, 214, 377, 446, 452, 579, 617, 620, 622, 623, 624, 625, 630, 632, 635, 636, 641, 642, 716, 717, 718, 730, 797, 812, 838, 841, 849, 851, 857, 872], "num": [54, 77, 138, 139, 630, 777, 822, 838, 851], "endpoint": [54, 77, 138, 139, 630, 792, 838], "logspac": [54, 77, 630, 851], "sequenc": [54, 58, 62, 63, 65, 71, 72, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94, 95, 104, 111, 112, 113, 114, 115, 116, 117, 118, 119, 133, 135, 137, 139, 142, 144, 150, 154, 156, 169, 173, 174, 181, 215, 221, 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573, 577, 578, 592, 593, 594, 596, 598, 600, 601, 614, 615, 618, 619, 620, 625, 630, 633, 635, 636, 637, 638, 640, 642, 648, 649, 650, 651, 652, 653, 654, 655, 657, 659, 660, 661, 662, 664, 667, 668, 669, 674, 675, 676, 677, 678, 679, 681, 683, 685, 686, 692, 695, 697, 698, 699, 700, 701, 703, 704, 706, 707, 708, 709, 710, 711, 714, 715, 719, 726, 736, 739, 740, 741, 742, 744, 747, 750, 751, 752, 753, 754, 758, 759, 761, 762, 763, 764, 765, 766, 767, 768, 769, 793, 796, 798, 822, 830, 831, 832, 833, 835, 846, 847, 849, 851, 856, 875], "on_valu": [54, 77, 139, 142, 630], "off_valu": [54, 77, 139, 142, 630], "evenli": [54, 57, 58, 62, 65, 75, 77, 80, 81, 85, 88, 127, 138, 139, 293, 376, 419, 423, 630, 633, 637, 640, 650, 651, 652, 653, 655, 657, 659, 709], "hint": [54, 57, 58, 63, 80, 81, 127, 128, 129, 131, 132, 133, 134, 136, 137, 138, 139, 140, 143, 144, 145, 146, 147, 149, 150, 156, 172, 176, 221, 222, 223, 224, 226, 227, 228, 229, 230, 231, 232, 233, 235, 236, 237, 238, 239, 241, 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846, 859], "464": [54, 57, 90, 139, 228, 229, 633], "15888336": [54, 139], "2154": [54, 139], "43469003": [54, 139], "meshgrid": [54, 77, 630], "spars": [54, 58, 64, 77, 81, 87, 140, 317, 370, 377, 435, 446, 452, 630, 639, 699], "xy": [54, 77, 140, 630], "coordin": [54, 57, 68, 80, 81, 91, 140, 148, 229, 291, 321, 322, 329, 350, 370, 384, 514, 630, 633, 645, 748], "conserv": [54, 140, 630], "cartesian": [54, 140, 630], "matrix": [54, 58, 59, 62, 63, 81, 82, 85, 86, 98, 99, 101, 103, 140, 146, 147, 148, 329, 330, 370, 377, 379, 388, 427, 430, 431, 434, 435, 436, 438, 441, 442, 443, 444, 445, 446, 447, 448, 451, 452, 483, 523, 535, 541, 630, 635, 637, 638, 661, 668, 670, 672, 673, 674, 675, 677, 678, 679, 680, 681, 682, 684, 685, 686, 687, 688, 689, 690, 692, 693, 696, 777, 779, 792, 793, 808, 812, 820, 831, 843, 870, 872], "ij": [54, 71, 140, 630, 648, 760, 808], "rank": [54, 58, 63, 65, 72, 81, 86, 88, 95, 98, 99, 100, 101, 102, 107, 140, 324, 325, 326, 327, 328, 370, 377, 379, 388, 435, 436, 446, 449, 452, 485, 493, 497, 533, 630, 638, 640, 645, 649, 669, 671, 679, 681, 685, 687, 692, 694, 695, 702, 703, 711, 714, 715, 748, 768, 769, 815, 880], "ni": [54, 140, 630], "xi": [54, 140, 630], "scatter": [54, 59, 77, 82, 142, 577, 578, 630, 635, 828, 842, 849, 879], "unless": [54, 58, 63, 77, 81, 142, 274, 335, 352, 357, 373, 630, 633, 638, 681, 827, 832, 842, 857, 866, 867], "ones_lik": [54, 77, 630, 827, 856, 869], "tril": [54, 77, 630], "whose": [54, 57, 58, 59, 63, 65, 69, 71, 77, 80, 81, 82, 86, 88, 92, 94, 99, 101, 103, 137, 146, 147, 223, 227, 230, 238, 239, 240, 279, 280, 286, 287, 291, 292, 293, 330, 344, 345, 349, 353, 354, 356, 360, 370, 377, 379, 430, 451, 484, 493, 499, 540, 596, 630, 633, 635, 638, 640, 646, 648, 668, 670, 672, 673, 674, 675, 676, 677, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 692, 695, 704, 708, 750, 751, 752, 759, 760, 779, 817, 834, 846], "innermost": [54, 58, 63, 86, 146, 147, 330, 370, 377, 430, 630, 638, 668, 670, 672, 673, 674, 675, 677, 679, 680, 681, 682, 684, 685, 686, 687, 688, 689, 692], "mxn": [54, 58, 63, 86, 146, 147, 330, 370, 630, 638, 672, 679, 681, 682, 684, 685, 689, 692], "matric": [54, 58, 63, 81, 86, 98, 99, 103, 140, 146, 147, 330, 370, 377, 379, 430, 435, 436, 438, 444, 445, 450, 474, 630, 637, 638, 661, 668, 670, 672, 673, 674, 675, 676, 677, 679, 680, 681, 682, 684, 685, 686, 687, 688, 689, 692, 693, 779, 818, 836, 872], "diagon": [54, 58, 63, 81, 86, 99, 133, 146, 147, 148, 314, 329, 330, 370, 377, 379, 428, 431, 441, 447, 474, 630, 638, 671, 692], "triangular": [54, 58, 63, 86, 146, 147, 148, 329, 330, 370, 377, 447, 630, 638, 668, 674, 675, 681, 685], "triu": [54, 77, 630], "upper": [54, 58, 63, 67, 81, 86, 90, 133, 147, 148, 314, 330, 370, 377, 388, 447, 526, 630, 638, 644, 668, 674, 675, 685, 742, 831, 842, 846], "zeros_lik": [54, 58, 77, 153, 270, 379, 493, 616, 617, 620, 622, 623, 624, 630, 631, 633, 636, 638, 640, 685, 700, 843, 849], "data_typ": [55, 58, 78, 81, 183, 631, 828, 831, 846, 847], "_arraywithdatatyp": [55, 103], "irrespect": [55, 63, 78, 86, 153, 631, 638, 688, 829, 842, 853, 879], "promot": [55, 57, 58, 63, 78, 80, 81, 86, 93, 103, 104, 153, 156, 179, 180, 181, 187, 222, 223, 224, 226, 227, 228, 229, 230, 231, 233, 234, 235, 236, 238, 239, 241, 244, 246, 248, 262, 263, 264, 265, 266, 271, 274, 279, 283, 286, 287, 288, 289, 290, 291, 292, 295, 347, 355, 360, 373, 376, 388, 420, 523, 586, 609, 631, 633, 635, 638, 640, 648, 668, 669, 676, 677, 678, 679, 680, 681, 683, 684, 686, 687, 694, 695, 701, 711, 754, 762, 765, 777, 778, 823, 825, 834, 835, 839, 848], "nan": [55, 57, 58, 59, 69, 71, 78, 80, 81, 82, 153, 221, 222, 223, 224, 226, 227, 228, 229, 230, 237, 238, 239, 240, 241, 242, 244, 246, 247, 248, 249, 250, 255, 256, 257, 262, 263, 264, 265, 266, 269, 274, 275, 277, 279, 280, 283, 284, 285, 286, 287, 288, 291, 292, 294, 301, 335, 336, 337, 348, 352, 357, 360, 368, 373, 379, 388, 493, 521, 522, 529, 530, 531, 532, 559, 614, 628, 631, 633, 635, 646, 648, 649, 750, 751, 752, 753, 761, 762, 763, 765, 766, 767, 768, 769, 777, 780, 825, 831, 834, 841, 847, 848], "infin": [55, 57, 59, 63, 78, 80, 86, 153, 221, 222, 223, 224, 227, 228, 229, 230, 237, 238, 239, 241, 242, 244, 246, 247, 248, 255, 256, 262, 263, 264, 265, 266, 269, 274, 275, 277, 279, 283, 284, 286, 287, 288, 291, 292, 294, 336, 337, 360, 373, 559, 628, 631, 633, 635, 638, 648, 649, 686, 695, 761, 763, 768, 769, 825, 834], "desir": [55, 56, 58, 68, 71, 75, 78, 79, 81, 91, 94, 98, 153, 155, 156, 215, 320, 361, 370, 373, 379, 388, 483, 529, 532, 533, 631, 632, 638, 645, 648, 690, 747, 762, 792, 793, 822, 827, 830, 831, 832, 843, 851, 861, 865, 872], "broadcast_arrai": [55, 78, 631], "mix": [55, 57, 78, 80, 81, 82, 87, 90, 103, 104, 154, 167, 168, 181, 200, 201, 231, 234, 235, 236, 241, 242, 248, 252, 260, 261, 271, 274, 277, 283, 378, 388, 459, 530, 549, 551, 552, 553, 554, 563, 598, 601, 631, 632, 633, 635, 637, 638, 639, 640, 643, 648, 651, 653, 656, 658, 659, 661, 667, 668, 690, 697, 699, 700, 738, 760, 762, 765, 778, 780, 820, 824, 831, 832, 833, 842, 849, 851, 859, 872, 876, 878], "broadcast_to": [55, 78, 631, 831], "can_cast": [55, 78, 631, 831, 839, 843], "accord": [55, 58, 59, 65, 71, 78, 88, 94, 156, 166, 224, 235, 241, 248, 274, 285, 320, 370, 376, 379, 421, 485, 553, 556, 577, 578, 631, 633, 635, 638, 640, 648, 694, 702, 715, 765, 767, 772, 779, 799, 807, 820, 821, 825, 831, 837, 839, 843, 846], "finfo": [55, 78, 631, 846], "resolut": [55, 78, 166, 631, 822], "4028235e": [55, 166, 631], "iinfo": [55, 78, 631], "integ": [55, 57, 58, 62, 63, 65, 67, 71, 72, 75, 80, 81, 82, 85, 86, 88, 90, 94, 95, 103, 104, 127, 136, 169, 170, 176, 180, 181, 185, 221, 231, 232, 233, 234, 235, 236, 237, 247, 248, 259, 271, 276, 279, 283, 284, 294, 295, 331, 332, 333, 336, 337, 341, 346, 347, 370, 373, 376, 379, 383, 386, 388, 404, 409, 419, 422, 423, 424, 471, 480, 485, 493, 497, 500, 509, 510, 511, 512, 513, 515, 516, 521, 523, 524, 525, 530, 533, 556, 572, 582, 615, 630, 631, 633, 635, 637, 638, 640, 644, 647, 648, 649, 650, 651, 652, 653, 655, 657, 659, 669, 671, 680, 694, 695, 709, 739, 740, 741, 742, 743, 744, 756, 758, 759, 761, 762, 763, 764, 765, 766, 767, 768, 769, 777, 778, 779, 780, 785, 793, 808, 822, 829, 831, 841, 844, 846, 851, 853], "119": [55, 169], "1220": [55, 169], "int16": [55, 58, 67, 71, 78, 90, 156, 160, 162, 167, 169, 176, 191, 388, 524, 525, 631, 648, 740, 758, 759, 764, 766, 777, 778, 831, 843, 846, 851], "32768": [55, 78, 169, 594, 635], "32767": [55, 78, 169], "is_bool_dtyp": [55, 78, 631], "is_float_dtyp": [55, 78, 631, 847], "is_int_dtyp": [55, 78, 631, 844, 847], "is_uint_dtyp": [55, 78, 631, 844, 847], "result_typ": [55, 78, 631, 831], "arrays_and_dtyp": [55, 78, 181, 631], "_arraywithdevic": [56, 103], "move": [56, 58, 79, 81, 148, 211, 215, 219, 329, 370, 379, 484, 630, 632, 795, 822, 832, 847], "addit": [56, 58, 59, 66, 79, 81, 82, 89, 124, 126, 215, 224, 284, 378, 382, 388, 453, 507, 522, 527, 546, 547, 548, 615, 629, 632, 633, 635, 637, 641, 643, 664, 718, 738, 793, 808, 820, 821, 822, 827, 831, 833, 834, 837, 839, 841, 842, 843, 846, 847, 849, 853, 854, 856, 865, 872, 873, 874, 878], "__dlpack__": [56, 79, 134, 215, 630, 632], "caveat": [56, 79, 215, 378, 457, 632], "portabl": [56, 79, 215, 632, 814, 870], "_arraywithelementwis": [57, 103], "ab": [57, 63, 73, 80, 96, 103, 104, 279, 335, 352, 373, 379, 492, 633, 638, 642, 679, 689, 695, 727, 730, 774, 807, 808, 818, 826, 831, 836, 840, 843, 846, 869], "absolut": [57, 58, 63, 73, 75, 80, 81, 86, 103, 221, 285, 335, 352, 355, 361, 373, 377, 378, 431, 448, 454, 456, 633, 638, 679, 680, 681, 686, 772, 774, 777, 779, 780, 815, 821], "aco": [57, 80, 633], "invers": [57, 58, 63, 80, 81, 86, 222, 223, 226, 227, 228, 229, 230, 345, 373, 376, 386, 399, 408, 410, 420, 515, 633, 638, 677, 680, 684, 799, 831], "cosin": [57, 80, 222, 223, 238, 239, 313, 316, 370, 376, 398, 408, 633, 793], "acosh": [57, 80, 167, 168, 631, 633, 818, 836], 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"Load-the-image-example-\ud83d\uddbc\ufe0f"], [8, "Load-the-image-example-\ud83d\uddbc\ufe0f"]], "Visualise image": [[12, "Visualise-image"], [8, "Visualise-image"]], "Model Inference ResNet34": [[12, "Model-Inference-ResNet34"]], "Initializing Native Torch ResNet34": [[12, "Initializing-Native-Torch-ResNet34"]], "Initializing Ivy ResNet34 with Pretrained Weights \u2b07\ufe0f": [[12, "Initializing-Ivy-ResNet34-with-Pretrained-Weights-\u2b07\ufe0f"]], "Use the model to classify your images \ud83d\ude80": [[12, "Use-the-model-to-classify-your-images-\ud83d\ude80"], [12, "id1"]], "Model Inference ResNet50": [[12, "Model-Inference-ResNet50"]], "Initializing Native Torch ResNet50": [[12, "Initializing-Native-Torch-ResNet50"]], "Initializing Ivy ResNet50 with Pretrained Weights \u2b07\ufe0f": [[12, "Initializing-Ivy-ResNet50-with-Pretrained-Weights-\u2b07\ufe0f"]], "0.2: Transpile": [[36, "0.2:-Transpile"]], "Write Ivy code": [[23, "Write-Ivy-code"]], "Contents": [[23, "Contents"]], "Installing Ivy": [[23, "Installing-Ivy"]], "Importing Ivy": [[23, "Importing-Ivy"], [0, "Importing-Ivy"]], "Ivy Backend Handler": [[23, "Ivy-Backend-Handler"], [32, "Ivy-Backend-Handler"]], "Data Structures": [[23, "Data-Structures"], [32, "Data-Structures"]], "Ivy Functional API": [[23, "Ivy-Functional-API"], [32, "Ivy-Functional-API"]], "Tutorials And Examples": [[21, "tutorials-and-examples"]], "Learn the basics": [[21, "learn-the-basics"], [22, "learn-the-basics"]], "Guides": [[21, "guides"], [16, "guides"]], "Examples and Demos": [[21, "examples-and-demos"], [3, "examples-and-demos"]], "Unify code": [[24, "Unify-code"]], "ODSC Ivy Demo": [[32, "ODSC-Ivy-Demo"]], "Graph Tracer": [[32, "Graph-Tracer"]], "Any function": [[32, "Any-function"], [33, "Any-function"]], "Any library": [[32, "Any-library"], [33, "Any-library"]], "Any model": [[32, "Any-model"], [33, "Any-model"]], "Demos": [[1, "demos"]], "Creating a Notebook for Demo": [[1, "creating-a-notebook-for-demo"]], "3.1: Stable Diffusion": [[43, "3.1:-Stable-Diffusion"]], "Transpile any library": [[29, "Transpile-any-library"]], "Accelerating PyTorch models with JAX": [[14, "Accelerating-PyTorch-models-with-JAX"]], "1.0: Lazy vs Eager": [[37, "1.0:-Lazy-vs-Eager"]], "Unify": [[37, "Unify"], [28, "Unify"], [38, "Unify"], [27, "Unify"], [39, "Unify"]], "Compile": [[37, "Compile"], [38, "Compile"], [39, "Compile"]], "Transpile": [[37, "Transpile"], [28, "Transpile"], [38, "Transpile"], [27, "Transpile"], [39, "Transpile"]], "Training PyTorch ResNet in your TensorFlow Projects": [[13, "Training-PyTorch-ResNet-in-your-TensorFlow-Projects"]], "Framework Incompatibility": [[13, "Framework-Incompatibility"], [6, "Framework-Incompatibility"]], "Transpiling a PyTorch model to TensorFlow": [[13, "Transpiling-a-PyTorch-model-to-TensorFlow"]], "About the transpiled model": [[13, "About-the-transpiled-model"], [6, "About-the-transpiled-model"]], "Setting-up the source model": [[13, "Setting-up-the-source-model"], [6, "Setting-up-the-source-model"]], "Load the Data": [[13, "Load-the-Data"]], "Visualize a few images": [[13, "Visualize-a-few-images"]], "Load the pre-trained model": [[13, "Load-the-pre-trained-model"]], "Converting the model from TensorFlow to PyTorch": [[13, "Converting-the-model-from-TensorFlow-to-PyTorch"], [6, "Converting-the-model-from-TensorFlow-to-PyTorch"]], "Comparing the results": [[13, "Comparing-the-results"], [7, "Comparing-the-results"], [6, "Comparing-the-results"]], "Fine-tuning the transpiled model": [[13, "Fine-tuning-the-transpiled-model"], [7, "Fine-tuning-the-transpiled-model"], [6, "Fine-tuning-the-transpiled-model"]], "Conclusion": [[13, "Conclusion"], [7, "Conclusion"], [6, "Conclusion"]], "TO REPLACE: Title": [[2, "TO-REPLACE:-Title"]], "How to use decorators": [[28, "How-to-use-decorators"]], "Trace": [[28, "Trace"], [27, "Trace"]], "0.0: Unify": [[34, "0.0:-Unify"]], "Transpile any model": [[30, "Transpile-any-model"]], "Round up": [[30, "Round-up"]], "Basic Operations with Ivy": [[44, "Basic-Operations-with-Ivy"]], "Installs \ud83d\udcbe": [[44, "Installs-\ud83d\udcbe"], [45, "Installs-\ud83d\udcbe"]], "Imports \ud83d\udec3": [[44, "Imports-\ud83d\udec3"], [45, "Imports-\ud83d\udec3"]], "Ivy as a Unified ML Framework \ud83d\udd00": [[44, "Ivy-as-a-Unified-ML-Framework-\ud83d\udd00"]], "Change frameworks by one line of code \u261d": [[44, "Change-frameworks-by-one-line-of-code-\u261d"]], "No need to worry about data types \ud83c\udfa8": [[44, "No-need-to-worry-about-data-types-\ud83c\udfa8"]], "No need to worry about framework differences \ud83d\udcb1": [[44, "No-need-to-worry-about-framework-differences-\ud83d\udcb1"]], "Unifying them all! \ud83c\udf72": [[44, "Unifying-them-all!-\ud83c\udf72"]], "Ivy as a standalone ML framework \ud83c\udf00": [[44, "Ivy-as-a-standalone-ML-framework-\ud83c\udf00"]], "Set Backend Framework": [[44, "Set-Backend-Framework"]], "Define Model": [[44, "Define-Model"], [45, "Define-Model"]], "Create Model": [[44, "Create-Model"]], "Create Optimizer": [[44, "Create-Optimizer"]], "Input and Target": [[44, "Input-and-Target"]], "Loss Function": [[44, "Loss-Function"]], "Training Loop": [[44, "Training-Loop"]], "How To Convert Models from PyTorch to PaddlePaddle": [[7, "How-To-Convert-Models-from-PyTorch-to-PaddlePaddle"]], "About the Model": [[7, "About-the-Model"]], "Transpiling the Model": [[7, "Transpiling-the-Model"]], "1.1: Framework Selection": [[38, "1.1:-Framework-Selection"]], "Compilation of a Basic Function": [[45, "Compilation-of-a-Basic-Function"]], "Import Ivy compiler": [[45, "Import-Ivy-compiler"]], "Function compilation \ud83d\udee0": [[45, "Function-compilation-\ud83d\udee0"]], "Set backend": [[45, "Set-backend"]], "Sample input": [[45, "Sample-input"]], "Define function to compile": [[45, "Define-function-to-compile"]], "Compile the function": [[45, "Compile-the-function"]], "Check results": [[45, "Check-results"], [45, "id1"]], "Compiling simple neural network \ud83e\udde0": [[45, "Compiling-simple-neural-network-\ud83e\udde0"]], "Create model": [[45, "Create-model"]], "Define input": [[45, "Define-input"]], "Compile network": [[45, "Compile-network"]], "Image Segmentation with Ivy UNet": [[8, "Image-Segmentation-with-Ivy-UNet"]], "Custom Preprocessing": [[8, "Custom-Preprocessing"]], "Model Inference": [[8, "Model-Inference"]], "Initializing Native Torch UNet": [[8, "Initializing-Native-Torch-UNet"]], "Initializing Ivy UNet with Pretrained Weights \u2b07\ufe0f": [[8, "Initializing-Ivy-UNet-with-Pretrained-Weights-\u2b07\ufe0f"]], "Custom masking function": [[8, "Custom-masking-function"]], "Use the model to segment your images \ud83d\ude80": [[8, "Use-the-model-to-segment-your-images-\ud83d\ude80"]], "TensorFlow backend": [[8, "TensorFlow-backend"]], "JAX": [[8, "JAX"]], "Appendix: the Ivy native implementation of UNet": [[8, "Appendix:-the-Ivy-native-implementation-of-UNet"]], "Accelerating MMPreTrain models with JAX": [[11, "Accelerating-MMPreTrain-models-with-JAX"]], "1.3: Dynamic vs Static": [[40, "1.3:-Dynamic-vs-Static"]], "Dynamic": [[40, "Dynamic"]], "Static": [[40, "Static"]], "ToDo: explain via examples why dynamic mode is set to True by default when transpiling to and from numpy and torch, but set to False by default when transpiling to and from tensorflow and jax.": [[40, "ToDo:-explain-via-examples-why-dynamic-mode-is-set-to-True-by-default-when-transpiling-to-and-from-numpy-and-torch,-but-set-to-False-by-default-when-transpiling-to-and-from-tensorflow-and-jax."]], "Lazy vs Eager": [[27, "Lazy-vs-Eager"]], "Using TensorFlow Models in your PyTorch Projects": [[6, "Using-TensorFlow-Models-in-your-PyTorch-Projects"]], "Transpiling a TensorFlow model to PyTorch": [[6, "Transpiling-a-TensorFlow-model-to-PyTorch"]], "1.2: As a Decorator": [[39, "1.2:-As-a-Decorator"]], "Transpiling a haiku model to build on top": [[18, "Transpiling-a-haiku-model-to-build-on-top"]], "Quickstart": [[33, "Quickstart"]], "Get familiar with Ivy": [[33, "Get-familiar-with-Ivy"]], "Functional API": [[33, "Functional-API"]], "Stateful API": [[33, "Stateful-API"]], "Tracing code": [[33, "Tracing-code"]], "Transpile code": [[26, "Transpile-code"]], "Transpiling a Tensorflow model to build on top": [[19, "Transpiling-a-Tensorflow-model-to-build-on-top"]], "Credit Card Fraud Detection using Ivy Framework": [[0, "Credit-Card-Fraud-Detection-using-Ivy-Framework"]], "Library Installation": [[0, "Library-Installation"]], "Importing Libraries and Configuring the Environment": [[0, "Importing-Libraries-and-Configuring-the-Environment"]], "Loading the Dataset": [[0, "Loading-the-Dataset"]], "Previewing the Dataset": [[0, "Previewing-the-Dataset"]], "Inspecting the End of the Dataset": [[0, "Inspecting-the-End-of-the-Dataset"]], "Dataset Information": [[0, "Dataset-Information"]], "Identifying Missing Values": [[0, "Identifying-Missing-Values"]], "Transaction Class Distribution": [[0, "Transaction-Class-Distribution"]], "Separating Data for Analysis": [[0, "Separating-Data-for-Analysis"]], "Statistical Measures of Legitimate Transactions": [[0, "Statistical-Measures-of-Legitimate-Transactions"]], "Statistical Measures of Fraudulent Transactions": [[0, "Statistical-Measures-of-Fraudulent-Transactions"]], "Comparing Transaction Metrics": [[0, "Comparing-Transaction-Metrics"]], "Under-Sampling for Balanced Dataset": [[0, "Under-Sampling-for-Balanced-Dataset"]], "Creating a Balanced Dataset": [[0, "Creating-a-Balanced-Dataset"]], "Splitting Data into Features and Targets": [[0, "Splitting-Data-into-Features-and-Targets"]], "Splitting Data into Training and Testing Sets": [[0, "Splitting-Data-into-Training-and-Testing-Sets"]], "Converting Data to Ivy Arrays": [[0, "Converting-Data-to-Ivy-Arrays"]], "Displaying Data Dimensions": [[0, "Displaying-Data-Dimensions"]], "Data Preparation Function": [[0, "Data-Preparation-Function"]], "Processing Training Data": [[0, "Processing-Training-Data"]], "Enabling Soft Device Mode in Ivy": [[0, "Enabling-Soft-Device-Mode-in-Ivy"]], "Configuring the XGBoost Classifier": [[0, "Configuring-the-XGBoost-Classifier"]], "Benchmarking XGBoost Model Training Time": [[0, "Benchmarking-XGBoost-Model-Training-Time"]], "Benchmarking Ivy-based XGBoost Model Training Time": [[0, "Benchmarking-Ivy-based-XGBoost-Model-Training-Time"]], "Benchmarking XGBoost Model Prediction Time": [[0, "Benchmarking-XGBoost-Model-Prediction-Time"]], "Benchmarking Ivy-based XGBoost Model Prediction Performance": [[0, "Benchmarking-Ivy-based-XGBoost-Model-Prediction-Performance"]], "Based on benchmark tests, the Ivy-based XGBoost implementation has demonstrated faster performance times compared to the standard XGBoost.": [[0, "Based-on-benchmark-tests,-the-Ivy-based-XGBoost-implementation-has-demonstrated-faster-performance-times-compared-to-the-standard-XGBoost."]], "Model Predictions and Classification Reports": [[0, "Model-Predictions-and-Classification-Reports"]], "Evaluation of Classifier Performance": [[0, "Evaluation-of-Classifier-Performance"]], "IvyClassifier Performance Metrics": [[0, "IvyClassifier-Performance-Metrics"]], "XGBClassifier Performance Metrics": [[0, "XGBClassifier-Performance-Metrics"]], "Visualization of Classification Reports": [[0, "Visualization-of-Classification-Reports"]], "Comparison of Ivy XGBoost and Standard XGBoost Classifiers": [[0, "Comparison-of-Ivy-XGBoost-and-Standard-XGBoost-Classifiers"]], "Ivy XGBoost Classifier:": [[0, "Ivy-XGBoost-Classifier:"]], "Standard XGBoost Classifier:": [[0, "Standard-XGBoost-Classifier:"]], "0.1: Compile": [[35, "0.1:-Compile"]], "Trace code": [[25, "Trace-code"]], "Ivy AlexNet demo": [[4, "Ivy-AlexNet-demo"]], "Ivy AlexNet inference in Torch": [[4, "Ivy-AlexNet-inference-in-Torch"]], "TensorFlow inference": [[4, "TensorFlow-inference"]], "JAX inference": [[4, "JAX-inference"]], "Appendix (Ivy code for AlexNet implementation)": [[4, "Appendix-(Ivy-code-for-AlexNet-implementation)"]], "Write a model using Ivy": [[31, "Write-a-model-using-Ivy"]], "Accelerating XGBoost with JAX": [[15, "Accelerating-XGBoost-with-JAX"]], "Tests": [[15, "Tests"]], "Loading the Data": [[15, "Loading-the-Data"]], "Comparing xgb_frontend.XGBClassifier and xgb.XGBClassifier": [[15, "Comparing-xgb_frontend.XGBClassifier-and-xgb.XGBClassifier"]], "JAX backend": [[15, "JAX-backend"]], "Tensorflow backend": [[15, "Tensorflow-backend"]], "PyTorch backend": [[15, "PyTorch-backend"]], "More exhaustive example": [[15, "More-exhaustive-example"]], "Evaluating Training Time vs. Number of Boosting Rounds": [[15, "Evaluating-Training-Time-vs.-Number-of-Boosting-Rounds"]], "Training Time vs. Fractions of Data": [[15, "Training-Time-vs.-Fractions-of-Data"]], "Comparison of Metrics": [[15, "Comparison-of-Metrics"]], "Transpiling a PyTorch model to build on top": [[17, "Transpiling-a-PyTorch-model-to-build-on-top"]], "# Ivy Bert Demo": [[5, "#-Ivy-Bert-Demo"]], "Install the dependecies": [[5, "Install-the-dependecies"]], "Import the modules": [[5, "Import-the-modules"]], "Ivy inference with Sequence Classification": [[5, "Ivy-inference-with-Sequence-Classification"]], "Ivy model inference with tensorflow": [[5, "Ivy-model-inference-with-tensorflow"]], "Ivy model inference with Jax": [[5, "Ivy-model-inference-with-Jax"]], "Ivy model inference with torch": [[5, "Ivy-model-inference-with-torch"]], "Developing a convolutional network using Ivy": [[20, "Developing-a-convolutional-network-using-Ivy"]], "2.0: Kornia": [[41, "2.0:-Kornia"]], "3.0: Perceiver": [[42, "3.0:-Perceiver"]], "End-to-End Training Pipeline in Ivy": [[48, "End-to-End-Training-Pipeline-in-Ivy"]], "Importing libraries": [[48, "Importing-libraries"]], "Let\u2019s build the pipeline with a Tensorflow backend": [[48, "Let's-build-the-pipeline-with-a-Tensorflow-backend"]], "We are using MNIST dataset for this Tutorial": [[48, "We-are-using-MNIST-dataset-for-this-Tutorial"]], "Temporary Dataset and Dynamic loader": [[48, "Temporary-Dataset-and-Dynamic-loader"]], "Defining the Ivy Network": [[48, "Defining-the-Ivy-Network"]], "Training Loop with utility functions": [[48, "Training-Loop-with-utility-functions"]], "Plotting the training metrics": [[48, "Plotting-the-training-metrics"]], "Save the trained Model": [[48, "Save-the-trained-Model"]], "Image": [[84, "module-ivy.data_classes.container.image"], [61, "module-ivy.data_classes.array.image"]], "Conversions": [[76, "module-ivy.data_classes.container.conversions"], [53, "module-ivy.data_classes.array.conversions"]], "Demo: Transpiling DeepMind\u2019s PerceiverIO": [[46, "Demo:-Transpiling-DeepMind's-PerceiverIO"]], "Table of Contents": [[46, "Table-of-Contents"]], "Defining the model": [[46, "Defining-the-model"]], "Model construction": [[46, "Model-construction"]], "Some helper functions": [[46, "Some-helper-functions"]], "Transpiling the model": [[46, "Transpiling-the-model"]], "PyTorch pipeline": [[46, "PyTorch-pipeline"]], "Dataset download": [[46, "Dataset-download"]], "DataLoader": [[46, "DataLoader"]], "Training": [[46, "Training"]], "Ivy as a Transpiler Introduction": [[50, "Ivy-as-a-Transpiler-Introduction"]], "To use the transpiler:": [[50, "To-use-the-transpiler:"]], "Transpiler Interface": [[50, "Transpiler-Interface"]], "Telemetry": [[50, "Telemetry"]], "1. Transpile Functions \ud83d\udd22": [[50, "1.-Transpile-Functions-\ud83d\udd22"]], "2. Transpile Libraries \ud83d\udcda": [[50, "2.-Transpile-Libraries-\ud83d\udcda"]], "3. Transpile Models \ud83c\udf10": [[50, "3.-Transpile-Models-\ud83c\udf10"]], "HuggingFace Tensorflow DeiT": [[49, "HuggingFace-Tensorflow-DeiT"]], "Graph can be visualized and displayed as html file on browser": [[49, "Graph-can-be-visualized-and-displayed-as-html-file-on-browser"]], "Deepmind PerceiverIO on GPU": [[47, "Deepmind-PerceiverIO-on-GPU"]], "Install Python3.8 and setup the kernel": [[47, "Install-Python3.8-and-setup-the-kernel"]], "Clone the ivy and ivy-models repo": [[47, "Clone-the-ivy-and-ivy-models-repo"]], "Install ivy and ivy_models from the repos": [[47, "Install-ivy-and-ivy_models-from-the-repos"]], "Run the demo\u2026": [[47, "Run-the-demo..."]], "\u2026with torch backend": [[47, "...with-torch-backend"]], "\u2026.with tensorflow backend": [[47, "....with-tensorflow-backend"]], "\u2026with jax backend": [[47, "...with-jax-backend"]], "\u2026with numpy backend": [[47, "...with-numpy-backend"]], "Resnet 18": [[51, "Resnet-18"]]}, "indexentries": 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865, 868, 872], "document": [1, 6, 7, 13, 23, 32, 65, 248, 336, 337, 373, 615, 633, 635, 711, 815, 816, 819, 822, 828, 830, 831, 833, 842, 843, 844, 846, 854, 856], "sphinx": [1, 816, 828], "websit": [1, 50, 814, 821, 825, 862], "alreadi": [2, 6, 13, 14, 24, 27, 28, 29, 30, 32, 33, 38, 46, 48, 51, 58, 63, 75, 81, 86, 237, 247, 274, 284, 294, 379, 388, 464, 465, 485, 521, 530, 633, 638, 676, 683, 807, 808, 820, 821, 822, 827, 829, 831, 832, 838, 842, 843, 849, 857, 858, 872, 874, 879], "instal": [2, 7, 8, 9, 10, 11, 14, 15, 17, 19, 24, 25, 26, 27, 28, 29, 30, 32, 33, 46, 48, 49, 50, 51, 816, 821, 822, 827, 828, 836, 837], "skip": [2, 5, 13, 48, 58, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94, 95, 111, 112, 113, 114, 115, 116, 117, 118, 119, 135, 137, 142, 144, 150, 154, 156, 181, 215, 221, 222, 223, 224, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 246, 247, 248, 252, 253, 255, 256, 257, 258, 260, 261, 262, 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30, 33, 46, 48, 50, 51], "manual": [2, 6, 7, 13, 14, 15, 17, 19, 23, 24, 25, 26, 27, 28, 29, 30, 33, 642, 719, 729, 730, 820, 821, 822, 831, 837, 846, 855, 858], "mind": [2, 17, 19, 23, 29, 32, 36, 820, 821, 826, 829, 846, 858, 866], "click": [2, 4, 48, 820, 821, 822, 830, 834, 836, 837, 852], "runtim": [2, 4, 5, 8, 11, 12, 13, 14, 25, 32, 35, 46, 47, 824, 839, 846, 849, 872], "restart": [2, 4, 5, 8, 12, 13, 46, 47, 821, 836], "git": [2, 4, 5, 8, 12, 32, 46, 47, 48, 49, 814, 816, 819, 821, 822, 825, 828, 830, 836, 837, 846, 858], "clone": [2, 4, 8, 12, 32, 46, 48, 49, 814, 816, 822, 836, 858], "http": [2, 4, 5, 6, 7, 8, 11, 12, 13, 14, 19, 27, 28, 29, 30, 32, 33, 46, 47, 48, 49, 50, 51, 57, 58, 80, 81, 83, 148, 156, 244, 254, 255, 270, 329, 336, 337, 370, 373, 376, 379, 388, 420, 493, 523, 616, 617, 630, 631, 633, 636, 638, 640, 648, 686, 687, 715, 765, 814, 816, 821, 822, 825, 828, 830, 831, 834, 836, 858, 866], "github": [2, 4, 5, 8, 11, 12, 14, 32, 46, 47, 48, 49, 50, 814, 816, 817, 819, 822, 823, 825, 828, 830, 831, 833, 834, 836, 837, 845, 846, 858, 861, 880], "com": [2, 4, 5, 6, 7, 8, 11, 12, 14, 19, 32, 46, 47, 48, 49, 50, 814, 816, 821, 822, 825, 828, 830, 831, 836, 858], "unifyai": [2, 4, 8, 12, 32, 46, 47, 48, 49, 50, 814, 816, 821, 822, 828, 836, 858], "model": [2, 3, 4, 9, 15, 16, 21, 22, 23, 49, 51, 241, 274, 378, 454, 633, 790, 794, 795, 812, 854, 855, 859, 865, 866, 870, 871, 872, 873, 874, 875, 876, 878, 879], "depth": [2, 4, 6, 8, 12, 47, 54, 58, 62, 77, 81, 85, 142, 376, 379, 412, 472, 546, 558, 630, 635, 637, 655, 656, 822, 830, 854, 855, 856, 858], "repositori": [2, 4, 8, 12, 816, 820, 821, 822, 824, 825, 828, 836, 845, 863], "cd": [2, 4, 8, 12, 32, 49, 814, 816, 821, 822, 836, 858], "resnet": [3, 6, 14, 21, 32, 865, 866], "imag": [3, 4, 6, 7, 11, 14, 17, 21, 29, 32, 33, 46, 47, 48, 49, 50, 51, 58, 62, 80, 81, 85, 103, 221, 222, 223, 224, 227, 230, 239, 242, 244, 246, 255, 256, 257, 262, 264, 277, 284, 285, 287, 288, 292, 376, 395, 396, 412, 413, 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635, 637, 640, 660, 707, 802, 803, 824, 827, 831, 832, 846, 851, 856, 866, 870, 871, 874, 876], "mmpretrain": [3, 21], "segment": [3, 21, 58, 81, 331, 332, 333, 370, 828, 833], "unet": [3, 21], "alexnet": [3, 21], "written": [3, 4, 5, 6, 13, 21, 23, 32, 33, 46, 59, 379, 474, 821, 825, 826, 834, 837, 838, 842, 843, 847, 851, 853, 856, 857, 861, 866, 870, 872, 876, 878, 879], "xgboost": [3, 21], "paddlepaddl": [3, 21, 336, 337, 373, 821], "dinov2": [3, 7, 21], "project": [3, 12, 14, 21, 26, 27, 28, 29, 30, 32, 33, 36, 99, 637, 664, 793, 814, 816, 817, 820, 821, 822, 823, 826, 827, 828, 846, 855, 857, 861, 862, 863, 866, 868, 870, 872, 875, 879, 880], "convnext": [3, 6, 11, 13, 21], "finetun": [3, 21, 46], "video": [4, 8, 11, 12, 14, 17, 19, 23, 24, 25, 26, 27, 28, 29, 30, 33, 814, 815, 820, 821, 822, 825, 826, 827, 829, 830, 831, 832, 833, 834, 835, 837, 838, 839, 840, 841, 842, 843, 844, 846, 847, 849, 858, 870], "tutori": [4, 6, 7, 8, 11, 12, 13, 14, 17, 19, 23, 24, 25, 26, 27, 28, 29, 30, 33, 814, 822, 843, 858], "three": [4, 5, 21, 27, 37, 38, 48, 58, 140, 313, 370, 379, 465, 630, 821, 822, 829, 830, 831, 833, 843, 846, 849, 850, 851, 873, 878], "major": [4, 5, 645, 748, 831, 832, 844, 846, 857, 862, 869, 872], "ml": [4, 5, 6, 13, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 32, 33, 34, 35, 36, 37, 38, 39, 46, 48, 51, 815, 819, 843, 850, 851, 852, 854, 855, 856, 860, 862, 863, 866, 868, 869, 870, 871, 872, 875, 877, 879], "framework": [4, 5, 7, 9, 17, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 33, 34, 35, 36, 37, 39, 46, 48, 50, 53, 59, 171, 193, 203, 206, 217, 544, 560, 564, 596, 599, 631, 632, 635, 642, 721, 772, 774, 778, 785, 790, 797, 802, 803, 817, 818, 820, 821, 824, 825, 826, 827, 828, 830, 831, 832, 833, 835, 836, 838, 839, 840, 842, 843, 846, 847, 849, 850, 851, 853, 856, 857, 858, 859, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 873, 876], "sinc": [4, 8, 12, 13, 29, 30, 32, 33, 46, 48, 58, 81, 99, 373, 816, 821, 822, 825, 826, 827, 828, 829, 830, 831, 832, 835, 842, 843, 857, 862, 872, 878], "automat": [4, 8, 9, 12, 13, 30, 32, 33, 38, 820, 821, 822, 824, 827, 828, 830, 831, 837, 839, 842, 846, 849, 850, 852, 855, 856, 858, 859, 863, 872, 875, 879], "sure": [4, 8, 11, 12, 13, 14, 15, 32, 46, 817, 820, 821, 822, 825, 830, 835, 836, 843, 844, 846, 849, 858], "enabl": [4, 5, 6, 8, 11, 12, 13, 14, 15, 27, 28, 30, 47, 58, 63, 75, 86, 104, 376, 378, 399, 457, 581, 635, 638, 681, 795, 812, 814, 821, 822, 823, 826, 829, 831, 839, 840, 841, 842, 843, 846, 847, 850, 852, 854, 856, 857, 859, 862, 865, 870, 871, 872, 873, 874, 875, 878, 879], "dm": [4, 5, 8, 11, 14, 32, 33, 44, 46], "haiku": [4, 5, 8, 11, 14, 30, 32, 33, 44, 46, 50, 790, 814, 856, 863, 866, 872], "exit": [4, 8, 12, 13, 32, 33, 832], "download": [4, 6, 7, 12, 13, 17, 19, 32, 33, 47, 48, 51, 816, 821, 828, 846, 865, 866], "imagenet": [4, 6, 13, 19, 47, 49, 814], "class": [4, 6, 7, 8, 12, 13, 15, 17, 19, 23, 32, 33, 44, 45, 46, 47, 48, 49, 52, 53, 54, 55, 56, 57, 58, 59, 60, 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795, 812, 820, 821, 822, 825, 827, 829, 830, 831, 833, 836, 838, 839, 840, 842, 843, 846, 847, 851, 854, 856, 857, 858, 861, 862, 863, 865, 866, 870, 872, 873, 876, 877, 878], "filterwarn": [4, 5, 13], "ignor": [4, 5, 13, 45, 53, 54, 58, 75, 81, 140, 376, 377, 379, 388, 400, 401, 402, 431, 439, 447, 487, 488, 492, 531, 630, 637, 642, 664, 730, 731, 797, 821, 828, 830, 833, 846, 857, 878], "compos": [4, 6, 7, 11, 12, 13, 32, 33, 46, 58, 81, 376, 390, 391, 392, 393, 821, 829, 843, 846, 865, 867, 872, 879], "resiz": [4, 6, 7, 8, 11, 12, 13, 46, 47, 58, 81, 376, 412, 849], "centercrop": [4, 12, 13], "224": [4, 6, 7, 12, 13, 17, 19, 32, 33, 46, 47, 49, 814, 866], "totensor": [4, 6, 7, 11, 12, 13, 46], "485": [4, 12, 13, 46], "456": [4, 12, 13, 46, 846], "406": [4, 12, 13, 46, 58, 81, 398, 541, 635], "229": [4, 12, 13, 46, 280, 633], "225": [4, 12, 13, 46, 48, 235, 633], "torch_img": [4, 8, 12], "unsqueez": [4, 8, 11, 12], "img": [4, 8, 12, 29, 32, 33, 46, 47, 48, 50, 814, 854, 866], "ipython": [4, 8, 12, 27, 28, 29, 30, 32, 33, 51], "displai": [4, 8, 12, 13, 29, 32, 33, 46, 47, 48, 50, 51, 821, 828, 830, 835, 846, 854], "end": [4, 8, 13, 46, 47, 58, 81, 127, 229, 285, 354, 373, 376, 378, 379, 424, 453, 475, 485, 487, 488, 630, 633, 808, 821, 822, 827, 830, 836, 842, 847, 849, 850, 857, 870, 875], "set_default_devic": [4, 5, 6, 8, 11, 12, 13, 14, 218, 632, 832], "ivy_model": [4, 5, 8, 12, 49], "ivy_alexnet": 4, "quick": [4, 21, 33, 822, 824, 844, 855], "trace_graph": [4, 5, 8, 12, 25, 26, 27, 28, 32, 33, 35, 36, 37, 38, 39, 40, 49, 795, 814, 851, 856, 864], "moment": [4, 58, 60, 81, 83, 377, 434, 616, 617, 622, 636, 797, 812, 820, 827, 857, 865, 866], "cost": [4, 60, 83, 616, 617, 620, 622, 623, 624, 636, 641, 716, 717, 718, 808, 831, 849, 870], "arg": [4, 6, 8, 9, 10, 11, 12, 13, 17, 19, 27, 28, 30, 32, 33, 37, 38, 39, 50, 53, 75, 97, 107, 123, 204, 214, 602, 629, 630, 632, 635, 772, 774, 789, 790, 793, 794, 795, 799, 802, 807, 812, 814, 826, 831, 832, 835, 841, 842, 843, 849, 851, 855, 865, 866, 867], "asarrai": [4, 5, 8, 11, 12, 47, 54, 58, 59, 70, 77, 81, 82, 93, 128, 386, 515, 516, 546, 557, 561, 562, 592, 593, 594, 630, 635, 637, 646, 647, 651, 751, 755, 835, 840, 843, 844], "cuda": [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 23, 32, 47, 48, 51, 54, 58, 67, 77, 81, 90, 138, 139, 142, 194, 195, 196, 212, 383, 509, 510, 512, 513, 630, 632, 638, 644, 689, 739, 740, 741, 742, 792, 793, 794, 795, 796, 797, 798, 812, 851, 857, 859, 877], "output": [4, 5, 7, 8, 9, 10, 12, 13, 23, 29, 30, 32, 33, 45, 46, 47, 49, 52, 54, 55, 56, 57, 58, 59, 60, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 74, 76, 77, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 89, 90, 91, 93, 94, 95, 103, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 127, 128, 129, 130, 131, 132, 133, 134, 136, 137, 138, 139, 140, 142, 143, 144, 145, 146, 147, 149, 150, 153, 155, 180, 214, 215, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 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731, 732, 736, 737, 738, 739, 740, 741, 742, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 772, 774, 779, 785, 792, 793, 794, 795, 798, 802, 807, 808, 810, 814, 818, 820, 821, 822, 824, 827, 828, 829, 831, 832, 833, 834, 835, 836, 839, 840, 841, 842, 843, 844, 846, 847, 848, 849, 850, 851, 852, 854, 855, 856, 857, 859, 866, 867, 870, 871, 872, 874, 878, 879], "282": [4, 12], "281": [4, 12, 46, 48], "285": [4, 12, 81], "64773697": 4, "29496649": 4, "04526037": 4, "tiger": [4, 12], "tabbi": [4, 7, 12], "egyptian": [4, 12], "torch_alexnet": 4, "alexnet_weight": 4, "imagenet1k_v1": [4, 12, 13], "dropout": [4, 62, 85, 376, 400, 401, 402, 637, 662, 664, 667, 793, 854], "torch_output": [4, 8, 9, 12], "dim": [4, 12, 48, 58, 75, 77, 81, 142, 314, 370, 376, 379, 394, 404, 405, 406, 409, 417, 475, 497, 630, 637, 650, 657, 658, 663, 779, 793, 831, 843, 844, 849], "torch_class": [4, 12], "torch_logit": [4, 12], "tensor": [4, 5, 6, 9, 11, 12, 13, 14, 17, 19, 23, 24, 27, 28, 30, 32, 33, 34, 38, 44, 46, 54, 57, 58, 59, 62, 63, 64, 65, 67, 71, 75, 77, 80, 81, 82, 85, 86, 87, 88, 90, 94, 97, 130, 138, 139, 142, 148, 164, 180, 272, 273, 303, 320, 324, 325, 326, 327, 328, 329, 338, 361, 368, 370, 373, 376, 377, 378, 379, 388, 389, 395, 396, 399, 403, 412, 413, 414, 415, 422, 424, 426, 433, 434, 435, 436, 439, 441, 443, 445, 446, 449, 451, 452, 453, 455, 458, 459, 475, 478, 483, 486, 487, 488, 489, 492, 497, 498, 529, 534, 577, 578, 630, 631, 633, 635, 637, 638, 639, 640, 644, 648, 660, 663, 664, 679, 690, 697, 707, 709, 739, 762, 793, 802, 808, 812, 814, 826, 827, 831, 832, 836, 838, 839, 842, 843, 844, 846, 847, 849, 851, 853, 854, 856, 857, 859, 861, 865, 866, 867, 869, 870, 873, 875, 876, 879], "6477": 4, "2950": 4, "0453": 4, "grad_fn": [4, 12, 30, 44, 619, 626, 636, 854], "takebackward0": [4, 12], "great": [4, 7, 8, 822, 846, 851, 853, 862, 863, 878], "simpl": [4, 7, 17, 21, 22, 24, 27, 29, 30, 31, 32, 33, 34, 35, 37, 38, 44, 46, 48, 51, 58, 81, 388, 523, 779, 793, 808, 814, 820, 821, 822, 826, 828, 829, 831, 832, 833, 834, 839, 842, 843, 846, 847, 849, 853, 855, 856, 857, 859, 861, 865, 866, 871, 872, 873, 874], "let": [4, 5, 6, 7, 8, 9, 11, 13, 14, 15, 17, 19, 23, 24, 25, 27, 28, 29, 30, 32, 33, 34, 35, 37, 38, 39, 44, 46, 47, 49, 51, 59, 71, 82, 221, 222, 223, 224, 227, 230, 239, 242, 244, 246, 255, 256, 257, 262, 264, 277, 285, 287, 288, 292, 553, 554, 633, 635, 638, 648, 692, 762, 764, 765, 766, 767, 814, 820, 823, 826, 828, 829, 830, 831, 832, 833, 834, 835, 836, 843, 844, 846, 847, 848, 849, 851, 853, 854, 855, 856, 863, 865, 866, 879], "ll": [4, 6, 7, 8, 9, 11, 13, 14, 23, 24, 25, 26, 27, 28, 29, 30, 32, 33, 34, 35, 36, 37, 38, 39, 47, 814, 815, 817, 818, 820, 821, 822, 823, 828, 833, 836, 837, 841, 842, 854, 858, 863, 865, 866], "try": [4, 6, 7, 13, 24, 34, 44, 47, 51, 75, 602, 635, 792, 802, 814, 820, 821, 822, 825, 826, 829, 830, 831, 835, 837, 842, 844, 851, 853, 857, 860, 862, 863, 867], "tf": [4, 6, 8, 9, 10, 13, 14, 17, 19, 24, 27, 28, 30, 32, 33, 34, 35, 37, 39, 44, 49, 50, 790, 814, 826, 831, 832, 838, 842, 843, 846, 847, 849, 851, 856, 857, 859, 865, 866, 867, 872], "onc": [4, 6, 8, 32, 33, 44, 46, 63, 67, 86, 90, 214, 377, 430, 632, 638, 644, 673, 674, 675, 688, 739, 814, 820, 821, 822, 829, 830, 831, 832, 833, 836, 837, 842, 843, 846, 849, 851, 854, 857, 858, 863, 865], "set": [4, 7, 9, 17, 19, 25, 32, 33, 35, 38, 46, 47, 48, 49, 50, 53, 58, 59, 62, 63, 68, 70, 71, 75, 81, 82, 85, 86, 91, 93, 94, 116, 119, 126, 146, 148, 182, 183, 184, 185, 186, 197, 210, 211, 212, 213, 214, 229, 329, 341, 357, 359, 364, 370, 373, 374, 376, 377, 378, 379, 388, 399, 420, 424, 428, 432, 435, 453, 458, 459, 475, 485, 488, 495, 523, 528, 529, 530, 531, 532, 533, 535, 539, 546, 558, 563, 579, 580, 581, 583, 584, 585, 586, 587, 588, 589, 590, 596, 604, 627, 629, 630, 631, 632, 633, 635, 637, 638, 642, 644, 645, 647, 648, 660, 667, 669, 679, 681, 684, 687, 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284, 285, 286, 287, 288, 289, 290, 291, 292, 294, 295, 296, 297, 298, 299, 300, 303, 304, 305, 306, 307, 308, 310, 311, 312, 314, 326, 335, 336, 337, 338, 339, 341, 343, 351, 352, 358, 360, 362, 363, 364, 370, 379, 399, 400, 401, 402, 420, 453, 454, 455, 456, 457, 458, 459, 460, 463, 464, 465, 469, 470, 485, 491, 493, 494, 495, 497, 502, 504, 505, 506, 508, 510, 523, 524, 525, 526, 535, 536, 538, 539, 541, 542, 546, 547, 548, 549, 550, 551, 552, 553, 554, 557, 559, 561, 562, 563, 565, 566, 569, 573, 577, 578, 592, 593, 594, 596, 598, 600, 601, 614, 625, 629, 631, 632, 635, 642, 651, 652, 653, 654, 660, 661, 667, 668, 669, 674, 675, 676, 677, 678, 679, 681, 683, 685, 686, 692, 697, 698, 699, 700, 704, 707, 708, 709, 710, 711, 714, 715, 719, 720, 722, 725, 726, 727, 728, 730, 731, 732, 736, 737, 739, 740, 741, 742, 744, 747, 750, 751, 752, 753, 754, 758, 759, 762, 764, 765, 767, 768, 769, 774, 775, 790, 793, 795, 802, 808, 826, 829, 854, 855, 859, 865, 866, 867], "recurs": [23, 32, 33, 46, 48, 53, 75, 76, 167, 168, 200, 201, 377, 449, 551, 552, 558, 631, 632, 635, 642, 719, 720, 723, 729, 730, 731, 772, 821, 825, 828, 829, 836, 839, 842, 855, 857], "fashion": [23, 779, 846, 866], "native_arrai": [23, 32, 33, 54, 55, 57, 77, 79, 80, 81, 82, 86, 93, 111, 114, 137, 140, 142, 144, 150, 153, 154, 155, 156, 164, 169, 176, 198, 207, 215, 231, 235, 240, 241, 242, 244, 248, 252, 260, 261, 269, 274, 277, 280, 283, 288, 336, 337, 364, 373, 378, 379, 459, 485, 491, 495, 535, 538, 565, 566, 569, 600, 627, 630, 631, 632, 633, 635, 637, 638, 639, 640, 644, 645, 648, 649, 651, 652, 659, 667, 670, 674, 675, 680, 681, 685, 689, 690, 692, 695, 697, 699, 700, 707, 739, 748, 757, 763, 766, 768, 774, 784, 802, 818, 836, 844, 846], "data_class": [23, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 106, 107, 108, 396, 397, 546, 550, 688, 713], "low": [23, 32, 35, 51, 58, 62, 67, 81, 85, 90, 376, 419, 423, 637, 644, 650, 651, 652, 653, 655, 657, 659, 740, 742, 779, 829, 835, 842, 843, 849, 851, 868, 870, 872, 873, 874, 876, 878], "c": [23, 32, 38, 47, 48, 54, 58, 59, 60, 62, 65, 71, 77, 78, 80, 81, 82, 83, 85, 86, 88, 92, 94, 98, 99, 117, 128, 129, 139, 142, 166, 169, 224, 235, 241, 242, 262, 263, 265, 274, 277, 285, 292, 376, 377, 379, 382, 388, 390, 391, 392, 393, 404, 409, 425, 427, 429, 430, 432, 444, 463, 464, 465, 475, 493, 497, 502, 503, 504, 507, 525, 538, 546, 547, 548, 549, 557, 561, 562, 592, 601, 616, 617, 620, 622, 623, 624, 627, 630, 631, 633, 635, 636, 637, 638, 640, 642, 645, 646, 648, 651, 652, 653, 654, 655, 656, 658, 673, 675, 677, 707, 711, 719, 722, 726, 727, 728, 730, 731, 736, 737, 748, 753, 759, 760, 765, 767, 796, 807, 808, 815, 821, 824, 827, 828, 829, 833, 839, 841, 850, 851, 852, 854, 857, 859, 860, 862, 863, 866, 868, 872, 876, 877, 879], "fundament": [23, 32, 830, 843, 849, 851, 861, 872], "signatur": [23, 32, 379, 388, 485, 523, 831, 832, 833, 834, 838, 842, 846, 847, 849, 862, 869, 878], "matmul": [23, 32, 33, 49, 63, 86, 377, 447, 615, 635, 638, 688, 827, 846, 847, 851], "to_n": [23, 32, 33, 44, 53, 76, 851], "jaxlib": [23, 29, 47, 802, 821, 826, 831, 832, 838, 847, 851, 853], "xla_extens": [23, 29, 802, 826, 831, 832, 838, 847, 851, 853], "arrayimpl": [23, 29, 802], "disabl": [23, 32, 58, 81, 379, 493, 795, 812, 828], "array_mod": [23, 32, 579, 603, 635, 848], "set_array_mod": [23, 32, 603, 635, 848], "ultim": [23, 32, 865], "sigmoid": [23, 32, 33, 44, 52, 58, 74, 81, 302, 368, 383, 509, 627, 789, 851, 854, 855], "z": [23, 32, 33, 45, 46, 54, 57, 58, 59, 63, 64, 67, 69, 71, 77, 80, 81, 82, 86, 87, 88, 90, 94, 103, 104, 138, 139, 141, 142, 202, 224, 225, 229, 231, 234, 236, 241, 252, 253, 256, 257, 258, 260, 261, 266, 268, 270, 271, 272, 273, 281, 290, 301, 302, 336, 337, 339, 368, 373, 378, 388, 454, 456, 457, 458, 459, 460, 466, 470, 481, 522, 523, 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241, 274, 284, 382, 383, 507, 509, 633, 638, 644, 669, 687, 739, 814, 825, 831, 833, 840, 851, 856, 866, 870], "div": [24, 25, 26, 27, 28, 32, 33, 34, 35, 36, 37, 38, 39, 867], "sub": [24, 25, 26, 27, 28, 32, 33, 34, 35, 36, 37, 38, 39, 58, 63, 65, 75, 76, 80, 81, 82, 86, 88, 104, 273, 377, 379, 388, 431, 471, 480, 500, 529, 530, 558, 635, 638, 640, 641, 672, 692, 709, 716, 717, 718, 820, 822, 824, 829, 835, 843, 844, 846, 853, 854, 855, 867, 868], "with_numpi": 24, "reproduc": [24, 49, 62, 85, 637, 660, 777, 778, 779, 780, 785, 818, 825, 836], "x_": [24, 34, 99, 285, 633, 867], "66391283": 24, "12516928": 24, "38367081": 24, "03102401": 24, "76419425": 24, "52797794": 24, "90346956": 24, "61316347": 24, "27585283": 24, "66309303": 24, "ivy_repo": 24, "sever": [24, 25, 34, 35, 37, 38, 39, 58, 81, 98, 376, 377, 390, 391, 392, 393, 445, 777, 821, 822, 847, 857, 870, 876], "pro": [24, 25, 26, 34, 35, 36, 37, 38, 39], "pick": [25, 35, 792], "trigger": [25, 35, 795, 820, 837], "unif": [25, 27, 28, 35, 37, 815, 853, 862, 868, 878], "55563945": 25, "65538704": 25, "14150524": 25, "46951997": 25, "30220294": 25, "14739668": 25, "57017946": 25, "91962677": 25, "51029003": 25, "59644395": 25, "constitu": [25, 35, 75, 856], "5556394": 25, "655387": 25, "1415051": 25, "4695197": 25, "3022028": 25, "1473966": 25, "5701794": 25, "91962665": 25, "51028997": 25, "5964439": 25, "985": 25, "000": [25, 80, 275, 777, 818, 830, 836], "On": [25, 32, 33, 821, 831, 832, 837, 843, 846, 849, 852, 856], "hand": [25, 57, 377, 447, 777, 825, 831, 832, 837, 839, 846, 857], "learnt": [26, 36], "ivy_norm": 26, "jax_norm": [26, 32, 33], "wider": [26, 36, 586, 609, 635, 831, 848, 878], "avoid": [26, 36, 38, 58, 65, 81, 241, 246, 248, 264, 274, 378, 379, 382, 455, 463, 464, 465, 471, 473, 475, 476, 477, 480, 484, 491, 500, 502, 503, 504, 540, 556, 558, 581, 586, 609, 633, 635, 640, 703, 704, 705, 707, 709, 710, 712, 714, 779, 780, 821, 822, 827, 828, 829, 830, 831, 835, 840, 843, 846, 847, 848, 849, 872], "act": [26, 36, 58, 81, 299, 364, 374, 822, 833, 848, 857, 879], "shorthand": [26, 36, 38, 846], "pair": [26, 36, 46, 58, 62, 81, 85, 229, 248, 321, 363, 370, 373, 376, 410, 419, 421, 423, 633, 637, 638, 650, 651, 652, 653, 655, 657, 659, 667, 669, 808], "93968587": 26, "26075466": 26, "22723222": 26, "06276492": 26, "47426987": 26, "72835908": 26, "71737559": 26, "50411096": 26, "65419174": 26, "15576624": 26, "implic": [26, 36, 37, 40, 829], "fw": [27, 28, 29, 30, 62, 85, 388, 523, 637, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 774, 821, 846], "mxnet": [27, 28, 29, 30, 210, 632, 802, 820, 821, 862, 879], "einop": [27, 28, 29, 30, 46, 48, 51, 59, 82, 546, 547, 548, 635, 831, 862], "miniconda": [27, 28, 29, 30], "multienv": [27, 28, 29, 30], "site": [27, 28, 29, 30, 873], "psutil": [27, 28, 29, 30, 46, 48, 51], "termcolor": [27, 28, 29, 30, 46, 48, 51, 75, 104], "colorama": [27, 28, 29, 30, 46, 48], "535": [27, 28, 29, 30, 52, 74, 119, 627, 835], "diskcach": [27, 28, 29, 30, 46], "auth": [27, 28, 29, 30], "urllib3": [27, 28, 29, 30, 46], "pyvi": [27, 28, 29, 30, 32, 33], "dill": [27, 28, 29, 30, 46], "astunpars": [27, 28, 29, 30], "cloudpickl": [27, 28, 29, 30], "gast": [27, 28, 29, 30], "wheel": [27, 28, 29, 30, 46, 48, 51, 861], "six": [27, 28, 29, 30, 46, 51, 821, 849], "cachetool": [27, 28, 29, 30], "pyasn1": [27, 28, 29, 30], "rsa": [27, 28, 29, 30], "jsonpickl": [27, 28, 29, 30], "charset": [27, 28, 29, 30, 46], "idna": [27, 28, 29, 30, 46], "certifi": [27, 28, 29, 30, 46], "2017": [27, 28, 29, 30, 46, 637, 664], "jedi": [27, 28, 29, 30], "inlin": [27, 28, 29, 30, 828], "prompt": [27, 28, 29, 30, 820, 822], "toolkit": [27, 28, 29, 30, 872, 873, 879], "pygment": [27, 28, 29, 30], "traitlet": [27, 28, 29, 30], "exceptiongroup": [27, 28, 29, 30], "pexpect": [27, 28, 29, 30], "parso": [27, 28, 29, 30], "ptyprocess": [27, 28, 29, 30], "wcwidth": [27, 28, 29, 30], "asttoken": [27, 28, 29, 30], "pure": [27, 28, 29, 30, 38, 48, 834, 838, 843, 849, 853, 856, 857, 872, 878, 879], "lazili": [27, 28, 29, 32, 33, 37, 39, 50, 814, 865, 866, 867], "actual": [27, 37, 818, 822, 824, 830, 836, 839, 840, 842, 843, 844, 846, 849, 850, 855, 857, 873, 878], "occur": [27, 32, 33, 37, 50, 55, 57, 69, 78, 80, 92, 156, 275, 291, 631, 633, 645, 646, 745, 746, 750, 751, 752, 753, 825, 830, 832, 835, 848], "altern": [27, 37, 47, 58, 81, 86, 98, 99, 335, 343, 344, 345, 349, 351, 352, 353, 354, 356, 357, 358, 362, 363, 373, 820, 821, 828, 842, 854, 875], "assum": [27, 28, 37, 38, 39, 54, 57, 58, 59, 62, 63, 64, 80, 81, 82, 85, 86, 87, 127, 128, 129, 131, 132, 133, 134, 136, 137, 138, 139, 140, 143, 144, 145, 146, 147, 149, 150, 156, 172, 176, 221, 222, 223, 224, 226, 227, 228, 229, 230, 231, 232, 233, 235, 236, 237, 238, 239, 241, 242, 244, 246, 247, 248, 252, 253, 254, 255, 256, 257, 258, 261, 263, 264, 265, 266, 268, 269, 270, 271, 274, 276, 277, 278, 279, 281, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 314, 330, 336, 337, 339, 342, 360, 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[27, 28], "36452447": [27, 28], "98795534": [27, 28], "15493582": [27, 28], "91630631": [27, 28], "41939619": [27, 28], "78909753": [27, 28], "19475674": [27, 28], "norm_trac": 27, "norm_tran": [27, 37], "know": [27, 28, 37, 38, 39, 69, 646, 750, 751, 752, 753, 814, 816, 820, 822, 832, 840, 844, 846, 849, 863, 867, 873], "07": [28, 46, 48, 60, 64, 80, 83, 87, 90, 229, 262, 265, 266, 285, 376, 408, 606, 616, 617, 619, 620, 621, 622, 633, 635, 636, 639, 698, 699, 741, 794, 797, 855], "981554": 28, "happen": [28, 32, 33, 293, 633, 814, 821, 822, 823, 832, 842, 846, 854, 863, 865, 866], "wherea": [28, 39, 81, 376, 422, 822, 826, 829, 831, 832, 833, 838, 839, 846, 856, 869], "subtract": [28, 32, 33, 57, 80, 103, 104, 135, 379, 485, 630, 633, 826, 829, 833], "often": [29, 58, 378, 453, 819, 825, 835, 838, 839, 843, 846, 857, 863, 873, 876, 879], "fortun": [29, 30, 825], "everyth": [29, 47, 807, 814, 820, 821, 822, 823, 824, 830, 833, 842, 843, 844, 846, 852, 857, 858, 863], "practic": [29, 822, 827, 830, 843, 845, 875], "jax_kornia": [29, 32, 33, 814, 866], "000000000034": [29, 32, 33, 814, 866], "raw_img": [29, 32, 33, 814, 866], "sharp": [29, 32, 33, 814], "prefer": [29, 32, 33, 248, 633, 821, 829, 835, 836, 840, 843, 858, 872], "whole": [30, 58, 81, 379, 382, 492, 505, 506, 508, 822, 828, 837], "full": [30, 58, 63, 81, 85, 86, 98, 99, 101, 166, 253, 261, 324, 325, 326, 327, 328, 370, 377, 378, 379, 450, 451, 457, 458, 486, 489, 580, 589, 604, 612, 630, 631, 633, 635, 637, 638, 652, 654, 655, 656, 658, 681, 685, 687, 688, 778, 785, 814, 821, 822, 828, 831, 834, 835, 838, 839, 843, 846, 849, 851, 857, 862, 863, 870, 872, 878], "complex": [30, 32, 33, 46, 52, 57, 58, 63, 71, 74, 78, 80, 81, 86, 94, 111, 112, 113, 114, 115, 116, 117, 118, 119, 143, 144, 159, 173, 182, 188, 221, 222, 223, 224, 225, 226, 227, 230, 238, 239, 241, 242, 244, 246, 254, 255, 256, 257, 258, 262, 263, 264, 265, 274, 276, 277, 279, 281, 284, 285, 286, 287, 288, 291, 292, 296, 301, 302, 304, 339, 344, 345, 368, 373, 376, 377, 388, 399, 410, 420, 421, 425, 430, 431, 432, 443, 445, 531, 532, 593, 594, 627, 630, 631, 633, 635, 638, 645, 648, 673, 674, 675, 679, 686, 688, 690, 692, 695, 748, 763, 764, 766, 778, 789, 808, 817, 820, 823, 828, 831, 833, 840, 843, 846, 847, 849, 854, 855, 856, 857, 859, 866, 868, 870, 872, 874, 878, 879], "neccessari": 30, "set_random_se": [30, 49], "301436": 30, "_c": 30, "0x7f252c392390": 30, "flatten": [30, 32, 33, 46, 48, 51, 58, 59, 63, 65, 68, 69, 81, 82, 86, 88, 91, 92, 341, 357, 373, 377, 379, 388, 428, 474, 484, 488, 493, 494, 497, 499, 521, 528, 529, 530, 531, 532, 533, 546, 550, 635, 638, 640, 645, 646, 676, 683, 695, 701, 706, 708, 745, 746, 750, 751, 752, 753, 772, 774, 814, 842, 849], "keyword": [30, 32, 33, 48, 50, 53, 54, 58, 75, 81, 104, 140, 275, 376, 379, 388, 424, 485, 523, 537, 540, 573, 602, 630, 633, 635, 638, 642, 648, 689, 725, 766, 772, 774, 778, 794, 795, 807, 820, 826, 829, 831, 832, 840, 842, 843, 844, 846, 847, 849, 854, 865, 866, 867], "input_arrai": [30, 32, 33, 842], "torch_model": [30, 32, 33, 50], "159": [30, 74, 111, 627, 637, 661], "thank": [30, 854, 862], "fledg": [30, 821, 851, 852], "output_arrai": [30, 32, 33, 58, 455], "0893": 30, "1504": 30, "1372": 30, "0991": 30, "0867": 30, "0851": 30, "0911": 30, "0804": 30, "0926": 30, "0881": 30, "softmaxbackward0": 30, "furthermor": 30, "relat": [30, 248, 633, 814, 816, 819, 820, 821, 822, 828, 835, 843, 846, 847, 848, 849, 866, 875], "regress": [31, 872, 879], "checkout": [32, 47, 822, 825, 846], "f705efe7cb5d18df17ce6c1e20f04d0eb4933f48": 32, "theoret": 32, "aspect": [32, 33, 815, 841, 854, 872], "easiest": [32, 814, 816, 821, 858], "defer": [32, 33, 820, 826, 831, 832, 839, 842, 843, 846, 878], "similarli": [32, 45, 140, 148, 224, 329, 336, 337, 370, 373, 630, 633, 827, 831, 843, 849, 853, 878], "essenc": [32, 873, 878], "becom": [32, 58, 81, 98, 347, 373, 379, 465, 640, 700, 802, 822, 823, 829, 831, 833, 835, 842, 857, 861, 863, 865], "slide": [32, 58, 62, 81, 85, 376, 395, 396, 397, 413, 414, 415, 416, 419, 423, 637, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 793], "regressor": [32, 33], "input_dim": [32, 33, 47], "output_dim": [32, 33, 47], "linear0": [32, 33, 44, 854, 855], "linear1": [32, 33, 44, 854, 855], "adam": [32, 33, 44, 48, 60, 83, 537, 616, 617, 622, 635, 636, 797, 854, 855, 856, 872], "n_training_exampl": [32, 33], "2000": [32, 33, 81, 315, 370], "random_norm": [32, 33, 62, 63, 67, 85, 86, 90, 546, 635, 637, 638, 644, 652, 654, 655, 656, 658, 659, 663, 688], "linspac": [32, 33, 54, 77, 127, 630, 838, 849, 851, 879], "execute_with_gradi": [32, 33, 44, 48, 636, 854, 855, 856, 857], "lambda": [32, 33, 49, 51, 81, 124, 126, 298, 308, 545, 558, 618, 619, 621, 626, 629, 635, 636, 638, 642, 674, 726, 727, 731, 820, 839, 840, 841, 844, 849, 851, 854], "2d": [32, 33, 48, 58, 81, 98, 314, 370, 376, 377, 379, 388, 391, 392, 400, 401, 443, 450, 464, 474, 523, 793, 812, 843, 849], "5f": [32, 33], "nonetheless": [32, 33], "gc": [32, 33, 558, 635], "decompos": [32, 33, 58, 81, 98, 101, 324, 325, 326, 327, 328, 349, 356, 370, 373, 377, 441, 446, 449, 452, 843, 856], "said": [32, 33, 779, 847, 863, 865], "otherwis": [32, 33, 50, 53, 54, 55, 57, 58, 59, 62, 63, 68, 69, 71, 72, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94, 95, 98, 111, 112, 113, 114, 115, 116, 117, 118, 119, 124, 127, 129, 130, 135, 137, 138, 139, 142, 144, 150, 153, 154, 156, 157, 159, 160, 161, 162, 163, 172, 176, 180, 181, 197, 215, 221, 222, 223, 224, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 246, 247, 248, 252, 253, 255, 256, 257, 258, 260, 261, 262, 263, 264, 265, 266, 268, 269, 270, 271, 272, 273, 274, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 294, 295, 296, 297, 298, 299, 300, 301, 304, 305, 306, 307, 308, 310, 311, 312, 314, 324, 325, 326, 327, 328, 335, 336, 337, 338, 339, 341, 342, 343, 351, 352, 358, 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"x0": [32, 33, 51, 82, 538, 635, 833], "normalize_trac": [32, 33], "html": [32, 33, 47, 57, 58, 80, 81, 148, 156, 244, 254, 255, 270, 329, 336, 337, 370, 373, 376, 379, 388, 420, 493, 523, 630, 631, 633, 638, 640, 648, 686, 687, 715, 765, 834, 862], "fname": [32, 33, 49, 51, 795, 854], "anticip": [32, 33], "addition": [32, 33, 829, 842, 843, 878], "normalize_native_comp": [32, 33], "return_backend_compiled_fn": 32, "immedi": [32, 33, 812, 814, 820, 821], "built": [32, 33, 38, 46, 48, 51, 127, 630, 793, 794, 795, 821, 822, 828, 829, 846, 852, 858, 865, 871, 872, 876], "eager_graph": [32, 33, 814, 865, 866], "lazy_graph": [32, 33, 814, 865, 866], "thought": [32, 33, 821, 822, 838, 862, 870], "matter": [32, 33, 38, 833, 861], "haven": [32, 33, 38, 858, 872], "jax_out": [32, 33], "ideal": [32, 33, 830, 831, 843, 849, 854], "worth": [32, 33], "differenti": [32, 33, 296, 366, 367, 368, 375, 872], "chosen": [32, 33, 51, 101, 127, 229, 630, 633, 645, 749, 820, 830, 843], "plai": [32, 33, 378, 457, 814, 817, 821, 823, 826, 832, 836, 843, 846, 856, 872, 875], "role": [32, 33, 814, 817, 822, 823, 832, 843, 852, 873, 875, 879], "dl": [32, 33], "effortlessli": [32, 33], "previous": [32, 33, 604, 635, 802, 820, 821, 827, 839, 841, 846, 851], "default_devic": [32, 33, 207, 210, 211, 212, 218, 219, 632, 832, 835, 836], "as_n": [32, 33, 55, 56, 75, 78, 79, 159, 160, 161, 162, 163, 164, 170, 197, 198, 631, 632, 831], "certainli": [32, 33, 862, 878], "unnecessari": [32, 33, 843], "extend": [32, 33, 58, 81, 379, 388, 485, 526, 827, 828, 831, 834, 835, 838, 843, 847, 857, 869, 872, 878], "infrastructur": [32, 33, 868, 874, 875], "least": [32, 57, 58, 63, 80, 81, 241, 259, 274, 376, 379, 388, 404, 409, 463, 464, 465, 474, 476, 523, 633, 638, 645, 678, 748, 822, 826, 830, 831, 832, 833, 839, 842, 846, 866], "coco": 32, "seamlessli": [33, 846], "therefor": [33, 38, 54, 57, 58, 63, 80, 81, 127, 128, 129, 131, 132, 133, 134, 136, 137, 138, 139, 140, 143, 144, 145, 146, 147, 148, 149, 150, 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832, 833, 834, 835, 838, 839, 840, 842, 843, 844, 846, 847, 849, 851, 853, 855, 857, 861, 869, 872, 878], "wide": [33, 814, 822, 846, 870, 872], "plenti": 33, "resourc": [33, 815, 820, 821, 830], "visit": [33, 820, 821, 822, 830], "page": [33, 814, 820, 821, 822, 828, 830, 836, 852, 853, 856, 858, 867, 880], "newli": [34, 35, 47, 49, 55, 78, 153, 540, 631, 635, 822, 830, 842, 846], "randon": [34, 35, 37, 38, 39], "mean_": 34, "std_": 34, "detect": [34, 38, 57, 75, 80, 256, 633, 642, 719, 730, 820, 821, 827, 829, 830, 837, 846, 854, 855], "inspect": [34, 38, 536, 635], "__": [34, 35, 36, 37, 38, 39, 75, 833, 854], "script": [35, 814, 821, 822, 825, 830, 833, 851, 857, 872], "comp": 35, "low_level": 35, "chain": [35, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94, 95, 98, 111, 112, 113, 114, 115, 116, 117, 118, 119, 135, 137, 142, 144, 150, 154, 156, 169, 173, 174, 181, 215, 221, 222, 223, 224, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 246, 247, 248, 252, 253, 255, 256, 257, 258, 260, 261, 262, 263, 264, 265, 266, 268, 269, 270, 271, 272, 273, 274, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 294, 295, 296, 297, 298, 300, 304, 305, 306, 307, 308, 310, 311, 312, 314, 335, 336, 337, 339, 341, 343, 351, 352, 358, 360, 362, 363, 364, 400, 401, 402, 420, 453, 454, 455, 456, 457, 458, 459, 460, 469, 470, 491, 493, 495, 497, 502, 504, 505, 506, 508, 510, 523, 524, 525, 526, 535, 538, 539, 541, 542, 546, 547, 548, 549, 550, 553, 554, 557, 559, 561, 562, 563, 565, 566, 569, 577, 578, 592, 593, 594, 596, 598, 600, 601, 614, 620, 625, 641, 642, 651, 652, 653, 654, 660, 661, 667, 668, 669, 674, 675, 676, 677, 678, 679, 681, 683, 685, 686, 692, 697, 698, 699, 700, 704, 707, 708, 709, 710, 711, 714, 715, 716, 717, 721, 732, 739, 740, 741, 742, 744, 747, 750, 751, 752, 753, 754, 758, 759, 762, 764, 765, 767, 768, 769, 798, 826, 829, 841, 843, 855, 856, 857, 872], "un": [35, 171, 631, 831, 851], "partial_comp": 35, "time_funct": 35, "express": [35, 57, 58, 80, 81, 99, 222, 226, 228, 229, 238, 240, 280, 286, 291, 360, 373, 633, 799, 808, 834, 843, 851, 856, 872, 873], "maxim": [35, 839, 842, 851, 869, 870, 874, 875, 876], "conclud": [36, 847], "norm_comp": [37, 38], "global": [37, 38, 48, 59, 75, 82, 104, 159, 160, 161, 162, 163, 212, 213, 214, 583, 584, 587, 593, 594, 606, 607, 610, 631, 632, 635, 785, 796, 802, 821, 826, 827, 830, 831, 832, 835, 839, 843, 851, 872], "b": [38, 52, 57, 58, 59, 62, 63, 71, 74, 77, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 98, 99, 102, 103, 104, 111, 112, 113, 114, 115, 116, 117, 118, 128, 129, 130, 135, 136, 137, 139, 142, 144, 150, 153, 154, 155, 156, 164, 174, 176, 181, 198, 215, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 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233, 234, 235, 236, 238, 239, 241, 244, 246, 248, 254, 255, 256, 257, 258, 262, 263, 264, 265, 266, 271, 274, 279, 283, 286, 287, 288, 289, 290, 291, 292, 295, 305, 309, 355, 360, 368, 373, 376, 377, 378, 379, 388, 412, 420, 431, 453, 454, 493, 497, 523, 535, 538, 559, 560, 564, 565, 566, 567, 568, 569, 596, 614, 630, 631, 632, 633, 635, 638, 640, 641, 646, 649, 668, 669, 670, 672, 676, 677, 678, 680, 681, 683, 684, 686, 687, 692, 694, 695, 701, 716, 717, 718, 750, 751, 752, 753, 754, 768, 769, 779, 785, 792, 796, 829, 831, 832, 834, 839, 843, 846, 848, 849, 861], "think": [38, 820, 822, 830, 833, 849, 873], "uniqu": [38, 48, 58, 59, 69, 81, 82, 92, 376, 377, 379, 424, 447, 484, 485, 499, 570, 635, 641, 642, 646, 716, 717, 718, 721, 725, 750, 751, 752, 753, 779, 814, 825, 829, 839, 843, 844, 845, 849, 857, 861, 875], "rule": [38, 55, 57, 58, 63, 78, 80, 81, 86, 153, 156, 179, 180, 181, 230, 241, 274, 276, 283, 285, 293, 295, 376, 379, 388, 420, 473, 523, 631, 633, 638, 640, 668, 669, 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822, 831, 842, 843, 846], "valueerror": [54, 58, 65, 77, 81, 88, 92, 129, 376, 378, 410, 421, 458, 463, 464, 471, 473, 475, 476, 477, 484, 500, 640, 703, 704, 705, 707, 709, 710, 712, 714, 753, 779, 809, 835], "buffer": [54, 77, 81, 88, 129, 135, 463, 464, 471, 473, 475, 476, 477, 484, 500, 630, 703, 704, 705, 707, 709, 710, 712, 714, 794, 795, 842, 857], "nativedtyp": [54, 55, 58, 62, 63, 67, 68, 71, 77, 81, 86, 90, 91, 94, 127, 128, 129, 131, 132, 133, 135, 136, 137, 138, 139, 141, 142, 143, 144, 149, 150, 152, 153, 158, 159, 160, 161, 162, 163, 164, 165, 170, 171, 175, 177, 179, 183, 193, 313, 314, 315, 316, 317, 318, 319, 334, 341, 357, 370, 373, 383, 388, 509, 510, 511, 512, 513, 523, 524, 525, 526, 529, 532, 630, 631, 637, 638, 644, 645, 647, 648, 660, 679, 695, 740, 741, 742, 745, 746, 756, 758, 759, 762, 764, 766, 792, 831, 832, 838, 847, 851], "datatyp": [54, 58, 75, 77, 81, 129, 137, 141, 158, 179, 183, 376, 424, 630, 631, 772, 847, 865], "nativedevic": [54, 56, 58, 67, 77, 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"isfinite": [[255, "isfinite"]], "expm1": [[246, "expm1"]], "bitwise_left_shift": [[233, "bitwise-left-shift"]], "isreal": [[258, "isreal"]], "lcm": [[259, "lcm"]], "bitwise_invert": [[232, "bitwise-invert"]], "bitwise_right_shift": [[235, "bitwise-right-shift"]], "greater_equal": [[253, "greater-equal"]], "set_split_factor": [[212, "set-split-factor"]], "acosh": [[223, "acosh"]], "num_gpus": [[206, "num-gpus"]], "add": [[224, "add"]], "function_supported_devices": [[200, "function-supported-devices"]], "num_cpu_cores": [[205, "num-cpu-cores"]], "split_factor": [[213, "split-factor"]], "valid_dtype": [[193, "valid-dtype"]], "clear_cached_mem_on_dev": [[196, "clear-cached-mem-on-dev"]], "unset_default_float_dtype": [[190, "unset-default-float-dtype"]], "atan2": [[229, "atan2"]], "gpu_is_available": [[203, "gpu-is-available"]], "asinh": [[227, "asinh"]], "tpu_is_available": [[217, "tpu-is-available"]], "dev": [[198, "dev"]], "unset_default_uint_dtype": [[192, "unset-default-uint-dtype"]], "num_ivy_arrays_on_dev": [[207, "num-ivy-arrays-on-dev"]], "atan": [[228, "atan"]], "split_func_call": [[214, "split-func-call"]], "unset_default_device": [[218, "unset-default-device"]], "as_ivy_dev": [[194, "as-ivy-dev"]], "print_all_ivy_arrays_on_dev": [[209, "print-all-ivy-arrays-on-dev"]], "set_default_float_dtype": [[184, "set-default-float-dtype"]], "set_soft_device_mode": [[211, "set-soft-device-mode"]], "angle": [[225, "angle"]], "abs": [[221, "abs"]], "handle_soft_device_variable": [[204, "handle-soft-device-variable"]], "to_device": [[215, "to-device"]], "set_default_uint_dtype": [[186, "set-default-uint-dtype"]], "unset_default_int_dtype": [[191, "unset-default-int-dtype"]], "total_mem_on_dev": [[216, "total-mem-on-dev"]], "unset_soft_device_mode": [[219, "unset-soft-device-mode"]], "asin": [[226, "asin"]], "type_promote_arrays": [[187, "type-promote-arrays"]], "default_device": [[197, "default-device"]], "get_all_ivy_arrays_on_dev": [[202, "get-all-ivy-arrays-on-dev"]], "unset_default_dtype": [[189, "unset-default-dtype"]], "dev_util": [[199, "dev-util"]], "percent_used_mem_on_dev": [[208, "percent-used-mem-on-dev"]], "used_mem_on_dev": [[220, "used-mem-on-dev"]], "set_default_int_dtype": [[185, "set-default-int-dtype"]], "function_unsupported_devices": [[201, "function-unsupported-devices"]], "acos": [[222, "acos"]], "set_default_device": [[210, "set-default-device"]], "unset_default_complex_dtype": [[188, "unset-default-complex-dtype"]], "as_native_dev": [[195, "as-native-dev"]], "How To Convert Models from PyTorch to PaddlePaddle": [[7, "How-To-Convert-Models-from-PyTorch-to-PaddlePaddle"]], "About the Model": [[7, "About-the-Model"]], "Transpiling the Model": [[7, "Transpiling-the-Model"]], "Comparing the results": [[7, "Comparing-the-results"], [6, "Comparing-the-results"], [13, "Comparing-the-results"]], "Fine-tuning the transpiled model": [[7, "Fine-tuning-the-transpiled-model"], [6, "Fine-tuning-the-transpiled-model"], [13, "Fine-tuning-the-transpiled-model"]], "Conclusion": [[7, "Conclusion"], [6, "Conclusion"], [13, "Conclusion"]], "Unify code": [[24, "Unify-code"]], "Credit Card Fraud Detection using Ivy Framework": [[0, "Credit-Card-Fraud-Detection-using-Ivy-Framework"]], "Library Installation": [[0, "Library-Installation"]], "Importing Libraries and Configuring the Environment": [[0, "Importing-Libraries-and-Configuring-the-Environment"]], "Loading the Dataset": [[0, "Loading-the-Dataset"]], "Previewing the Dataset": [[0, "Previewing-the-Dataset"]], "Inspecting the End of the Dataset": [[0, "Inspecting-the-End-of-the-Dataset"]], "Dataset Information": [[0, "Dataset-Information"]], "Identifying Missing Values": [[0, "Identifying-Missing-Values"]], "Transaction Class Distribution": [[0, "Transaction-Class-Distribution"]], "Importing Ivy": [[0, "Importing-Ivy"], [23, "Importing-Ivy"]], "Separating Data for Analysis": [[0, "Separating-Data-for-Analysis"]], "Statistical Measures of Legitimate Transactions": [[0, "Statistical-Measures-of-Legitimate-Transactions"]], "Statistical Measures of Fraudulent Transactions": [[0, "Statistical-Measures-of-Fraudulent-Transactions"]], "Comparing Transaction Metrics": [[0, "Comparing-Transaction-Metrics"]], "Under-Sampling for Balanced Dataset": [[0, "Under-Sampling-for-Balanced-Dataset"]], "Creating a Balanced Dataset": [[0, "Creating-a-Balanced-Dataset"]], "Splitting Data into Features and Targets": [[0, "Splitting-Data-into-Features-and-Targets"]], "Splitting Data into Training and Testing Sets": [[0, "Splitting-Data-into-Training-and-Testing-Sets"]], "Converting Data to Ivy Arrays": [[0, "Converting-Data-to-Ivy-Arrays"]], "Displaying Data Dimensions": [[0, "Displaying-Data-Dimensions"]], "Data Preparation Function": [[0, "Data-Preparation-Function"]], "Processing Training Data": [[0, "Processing-Training-Data"]], "Enabling Soft Device Mode in Ivy": [[0, "Enabling-Soft-Device-Mode-in-Ivy"]], "Configuring the XGBoost Classifier": [[0, "Configuring-the-XGBoost-Classifier"]], "Benchmarking XGBoost Model Training Time": [[0, "Benchmarking-XGBoost-Model-Training-Time"]], "Benchmarking Ivy-based XGBoost Model Training Time": [[0, "Benchmarking-Ivy-based-XGBoost-Model-Training-Time"]], "Benchmarking XGBoost Model Prediction Time": [[0, "Benchmarking-XGBoost-Model-Prediction-Time"]], "Benchmarking Ivy-based XGBoost Model Prediction Performance": [[0, "Benchmarking-Ivy-based-XGBoost-Model-Prediction-Performance"]], "Based on benchmark tests, the Ivy-based XGBoost implementation has demonstrated faster performance times compared to the standard XGBoost.": [[0, "Based-on-benchmark-tests,-the-Ivy-based-XGBoost-implementation-has-demonstrated-faster-performance-times-compared-to-the-standard-XGBoost."]], "Model Predictions and Classification Reports": [[0, "Model-Predictions-and-Classification-Reports"]], "Evaluation of Classifier Performance": [[0, "Evaluation-of-Classifier-Performance"]], "IvyClassifier Performance Metrics": [[0, "IvyClassifier-Performance-Metrics"]], "XGBClassifier Performance Metrics": [[0, "XGBClassifier-Performance-Metrics"]], "Visualization of Classification Reports": [[0, "Visualization-of-Classification-Reports"]], "Comparison of Ivy XGBoost and Standard XGBoost Classifiers": [[0, "Comparison-of-Ivy-XGBoost-and-Standard-XGBoost-Classifiers"]], "Ivy XGBoost Classifier:": [[0, "Ivy-XGBoost-Classifier:"]], "Standard XGBoost Classifier:": [[0, "Standard-XGBoost-Classifier:"]], "1.1: Framework Selection": [[38, "1.1:-Framework-Selection"]], "Unify": [[38, "Unify"], [37, "Unify"], [39, "Unify"], [27, "Unify"], [28, "Unify"]], "Compile": [[38, "Compile"], [37, "Compile"], [39, "Compile"]], "Transpile": [[38, "Transpile"], [37, "Transpile"], [39, "Transpile"], [27, "Transpile"], [28, "Transpile"]], "Examples and Demos": [[3, "examples-and-demos"], [21, "examples-and-demos"]], "Demos": [[1, "demos"]], "Creating a Notebook for Demo": [[1, "creating-a-notebook-for-demo"]], "Transpile code": [[26, "Transpile-code"]], "2.0: Kornia": [[41, "2.0:-Kornia"]], "1.0: Lazy vs Eager": [[37, "1.0:-Lazy-vs-Eager"]], "Guides": [[16, "guides"], [21, "guides"]], "Transpiling a PyTorch model to build on top": [[17, "Transpiling-a-PyTorch-model-to-build-on-top"]], "Using Ivy ResNet": [[12, "Using-Ivy-ResNet"]], "Installation": [[12, "Installation"], [4, "Installation"], [13, "Installation"]], "Imports": [[12, "Imports"], [8, "Imports"], [15, "Imports"]], "Data Preparation": [[12, "Data-Preparation"], [4, "Data-Preparation"], [8, "Data-Preparation"], [5, "Data-Preparation"]], "Prepare the set of labels": [[12, "Prepare-the-set-of-labels"]], "Load the image example \ud83d\uddbc\ufe0f": [[12, "Load-the-image-example-\ud83d\uddbc\ufe0f"], [8, "Load-the-image-example-\ud83d\uddbc\ufe0f"]], "Visualise image": [[12, "Visualise-image"], [8, "Visualise-image"]], "Model Inference ResNet34": [[12, "Model-Inference-ResNet34"]], "Initializing Native Torch ResNet34": [[12, "Initializing-Native-Torch-ResNet34"]], "Initializing Ivy ResNet34 with Pretrained Weights \u2b07\ufe0f": [[12, "Initializing-Ivy-ResNet34-with-Pretrained-Weights-\u2b07\ufe0f"]], "Use the model to classify your images \ud83d\ude80": [[12, "Use-the-model-to-classify-your-images-\ud83d\ude80"], [12, "id1"]], "Model Inference ResNet50": [[12, "Model-Inference-ResNet50"]], "Initializing Native Torch ResNet50": [[12, "Initializing-Native-Torch-ResNet50"]], "Initializing Ivy ResNet50 with Pretrained Weights \u2b07\ufe0f": [[12, "Initializing-Ivy-ResNet50-with-Pretrained-Weights-\u2b07\ufe0f"]], "Developing a convolutional network using Ivy": [[20, "Developing-a-convolutional-network-using-Ivy"]], "Ivy AlexNet demo": [[4, "Ivy-AlexNet-demo"]], "Ivy AlexNet inference in Torch": [[4, "Ivy-AlexNet-inference-in-Torch"]], "TensorFlow inference": [[4, "TensorFlow-inference"]], "JAX inference": [[4, "JAX-inference"]], "Appendix (Ivy code for AlexNet implementation)": [[4, "Appendix-(Ivy-code-for-AlexNet-implementation)"]], "Write a model using Ivy": [[31, "Write-a-model-using-Ivy"]], "Using TensorFlow Models in your PyTorch Projects": [[6, "Using-TensorFlow-Models-in-your-PyTorch-Projects"]], "Framework Incompatibility": [[6, "Framework-Incompatibility"], [13, "Framework-Incompatibility"]], "Transpiling a TensorFlow model to PyTorch": [[6, "Transpiling-a-TensorFlow-model-to-PyTorch"]], "About the transpiled model": [[6, "About-the-transpiled-model"], [13, "About-the-transpiled-model"]], "Setting-up the source model": [[6, "Setting-up-the-source-model"], [13, "Setting-up-the-source-model"]], "Converting the model from TensorFlow to PyTorch": [[6, "Converting-the-model-from-TensorFlow-to-PyTorch"], [13, "Converting-the-model-from-TensorFlow-to-PyTorch"]], "Image Segmentation with Ivy UNet": [[8, "Image-Segmentation-with-Ivy-UNet"]], "Custom Preprocessing": [[8, "Custom-Preprocessing"]], "Model Inference": [[8, "Model-Inference"]], "Initializing Native Torch UNet": [[8, "Initializing-Native-Torch-UNet"]], "Initializing Ivy UNet with Pretrained Weights \u2b07\ufe0f": [[8, "Initializing-Ivy-UNet-with-Pretrained-Weights-\u2b07\ufe0f"]], "Custom masking function": [[8, "Custom-masking-function"]], "Use the model to segment your images \ud83d\ude80": [[8, "Use-the-model-to-segment-your-images-\ud83d\ude80"]], "TensorFlow backend": [[8, "TensorFlow-backend"]], "JAX": [[8, "JAX"]], "Appendix: the Ivy native implementation of UNet": [[8, "Appendix:-the-Ivy-native-implementation-of-UNet"]], "Accelerating MMPreTrain models with JAX": [[11, "Accelerating-MMPreTrain-models-with-JAX"]], "0.1: Compile": [[35, "0.1:-Compile"]], "Transpiling a haiku model to build on top": [[18, "Transpiling-a-haiku-model-to-build-on-top"]], "Learn the basics": [[22, "learn-the-basics"], [21, "learn-the-basics"]], "Quickstart": [[33, "Quickstart"]], "Get familiar with Ivy": [[33, "Get-familiar-with-Ivy"]], "Functional API": [[33, "Functional-API"]], "Stateful API": [[33, "Stateful-API"]], "Tracing code": [[33, "Tracing-code"]], "Any function": [[33, "Any-function"], [32, "Any-function"]], "Any library": [[33, "Any-library"], [32, "Any-library"]], "Any model": [[33, "Any-model"], [32, "Any-model"]], "ODSC Ivy Demo": [[32, "ODSC-Ivy-Demo"]], "Ivy Backend Handler": [[32, "Ivy-Backend-Handler"], [23, "Ivy-Backend-Handler"]], "Data Structures": [[32, "Data-Structures"], [23, "Data-Structures"]], "Ivy Functional API": [[32, "Ivy-Functional-API"], [23, "Ivy-Functional-API"]], "Graph Tracer": [[32, "Graph-Tracer"]], "0.2: Transpile": [[36, "0.2:-Transpile"]], "Write Ivy code": [[23, "Write-Ivy-code"]], "Contents": [[23, "Contents"]], "Installing Ivy": [[23, "Installing-Ivy"]], "Basic Operations with Ivy": [[44, "Basic-Operations-with-Ivy"]], "Installs \ud83d\udcbe": [[44, "Installs-\ud83d\udcbe"], [45, "Installs-\ud83d\udcbe"]], "Imports \ud83d\udec3": [[44, "Imports-\ud83d\udec3"], [45, "Imports-\ud83d\udec3"]], "Ivy as a Unified ML Framework \ud83d\udd00": [[44, "Ivy-as-a-Unified-ML-Framework-\ud83d\udd00"]], "Change frameworks by one line of code \u261d": [[44, "Change-frameworks-by-one-line-of-code-\u261d"]], "No need to worry about data types \ud83c\udfa8": [[44, "No-need-to-worry-about-data-types-\ud83c\udfa8"]], "No need to worry about framework differences \ud83d\udcb1": [[44, "No-need-to-worry-about-framework-differences-\ud83d\udcb1"]], "Unifying them all! \ud83c\udf72": [[44, "Unifying-them-all!-\ud83c\udf72"]], "Ivy as a standalone ML framework \ud83c\udf00": [[44, "Ivy-as-a-standalone-ML-framework-\ud83c\udf00"]], "Set Backend Framework": [[44, "Set-Backend-Framework"]], "Define Model": [[44, "Define-Model"], [45, "Define-Model"]], "Create Model": [[44, "Create-Model"]], "Create Optimizer": [[44, "Create-Optimizer"]], "Input and Target": [[44, "Input-and-Target"]], "Loss Function": [[44, "Loss-Function"]], "Training Loop": [[44, "Training-Loop"]], "TO REPLACE: Title": [[2, "TO-REPLACE:-Title"]], "# Ivy Bert Demo": [[5, "#-Ivy-Bert-Demo"]], "Install the dependecies": [[5, "Install-the-dependecies"]], "Import the modules": [[5, "Import-the-modules"]], "Ivy inference with Sequence Classification": [[5, "Ivy-inference-with-Sequence-Classification"]], "Ivy model inference with tensorflow": [[5, "Ivy-model-inference-with-tensorflow"]], "Ivy model inference with Jax": [[5, "Ivy-model-inference-with-Jax"]], "Ivy model inference with torch": [[5, "Ivy-model-inference-with-torch"]], "Compilation of a Basic Function": [[45, "Compilation-of-a-Basic-Function"]], "Import Ivy compiler": [[45, "Import-Ivy-compiler"]], "Function compilation \ud83d\udee0": [[45, "Function-compilation-\ud83d\udee0"]], "Set backend": [[45, "Set-backend"]], "Sample input": [[45, "Sample-input"]], "Define function to compile": [[45, "Define-function-to-compile"]], "Compile the function": [[45, "Compile-the-function"]], "Check results": [[45, "Check-results"], [45, "id1"]], "Compiling simple neural network \ud83e\udde0": [[45, "Compiling-simple-neural-network-\ud83e\udde0"]], "Create model": [[45, "Create-model"]], "Define input": [[45, "Define-input"]], "Compile network": [[45, "Compile-network"]], "1.3: Dynamic vs Static": [[40, "1.3:-Dynamic-vs-Static"]], "Dynamic": [[40, "Dynamic"]], "Static": [[40, "Static"]], "ToDo: explain via examples why dynamic mode is set to True by default when transpiling to and from numpy and torch, but set to False by default when transpiling to and from tensorflow and jax.": [[40, "ToDo:-explain-via-examples-why-dynamic-mode-is-set-to-True-by-default-when-transpiling-to-and-from-numpy-and-torch,-but-set-to-False-by-default-when-transpiling-to-and-from-tensorflow-and-jax."]], "Accelerating XGBoost with JAX": [[15, "Accelerating-XGBoost-with-JAX"]], "Tests": [[15, "Tests"]], "Loading the Data": [[15, "Loading-the-Data"]], "Comparing xgb_frontend.XGBClassifier and xgb.XGBClassifier": [[15, "Comparing-xgb_frontend.XGBClassifier-and-xgb.XGBClassifier"]], "JAX backend": [[15, "JAX-backend"]], "Tensorflow backend": [[15, "Tensorflow-backend"]], "PyTorch backend": [[15, "PyTorch-backend"]], "More exhaustive example": [[15, "More-exhaustive-example"]], "Evaluating Training Time vs. Number of Boosting Rounds": [[15, "Evaluating-Training-Time-vs.-Number-of-Boosting-Rounds"]], "Training Time vs. Fractions of Data": [[15, "Training-Time-vs.-Fractions-of-Data"]], "Comparison of Metrics": [[15, "Comparison-of-Metrics"]], "Tutorials And Examples": [[21, "tutorials-and-examples"]], "Trace code": [[25, "Trace-code"]], "Training PyTorch ResNet in your TensorFlow Projects": [[13, "Training-PyTorch-ResNet-in-your-TensorFlow-Projects"]], "Transpiling a PyTorch model to TensorFlow": [[13, "Transpiling-a-PyTorch-model-to-TensorFlow"]], "Load the Data": [[13, "Load-the-Data"]], "Visualize a few images": [[13, "Visualize-a-few-images"]], "Load the pre-trained model": [[13, "Load-the-pre-trained-model"]], "1.2: As a Decorator": [[39, "1.2:-As-a-Decorator"]], "Transpiling a Tensorflow model to build on top": [[19, "Transpiling-a-Tensorflow-model-to-build-on-top"]], "Lazy vs Eager": [[27, "Lazy-vs-Eager"]], "Trace": [[27, "Trace"], [28, "Trace"]], "Transpile any model": [[30, "Transpile-any-model"]], "Round up": [[30, "Round-up"]], "0.0: Unify": [[34, "0.0:-Unify"]], "How to use decorators": [[28, "How-to-use-decorators"]], "3.1: Stable Diffusion": [[43, "3.1:-Stable-Diffusion"]], "3.0: Perceiver": [[42, "3.0:-Perceiver"]], "Accelerating PyTorch models with JAX": [[14, "Accelerating-PyTorch-models-with-JAX"]], "Transpile any library": [[29, "Transpile-any-library"]], "HuggingFace Tensorflow DeiT": [[49, "HuggingFace-Tensorflow-DeiT"]], "Graph can be visualized and displayed as html file on browser": [[49, "Graph-can-be-visualized-and-displayed-as-html-file-on-browser"]], "Image": [[61, "module-ivy.data_classes.array.image"], [84, "module-ivy.data_classes.container.image"]], "Ivy as a Transpiler Introduction": [[50, "Ivy-as-a-Transpiler-Introduction"]], "To use the transpiler:": [[50, "To-use-the-transpiler:"]], "Transpiler Interface": [[50, "Transpiler-Interface"]], "Telemetry": [[50, "Telemetry"]], "1. Transpile Functions \ud83d\udd22": [[50, "1.-Transpile-Functions-\ud83d\udd22"]], "2. Transpile Libraries \ud83d\udcda": [[50, "2.-Transpile-Libraries-\ud83d\udcda"]], "3. Transpile Models \ud83c\udf10": [[50, "3.-Transpile-Models-\ud83c\udf10"]], "Conversions": [[53, "module-ivy.data_classes.array.conversions"], [76, "module-ivy.data_classes.container.conversions"]], "Resnet 18": [[51, "Resnet-18"]], "Demo: Transpiling DeepMind\u2019s PerceiverIO": [[46, "Demo:-Transpiling-DeepMind's-PerceiverIO"]], "Table of Contents": [[46, "Table-of-Contents"]], "Defining the model": [[46, "Defining-the-model"]], "Model construction": [[46, "Model-construction"]], "Some helper functions": [[46, "Some-helper-functions"]], "Transpiling the model": [[46, "Transpiling-the-model"]], "PyTorch pipeline": [[46, "PyTorch-pipeline"]], "Dataset download": [[46, "Dataset-download"]], "DataLoader": [[46, "DataLoader"]], "Training": [[46, "Training"]], "End-to-End Training Pipeline in Ivy": [[48, "End-to-End-Training-Pipeline-in-Ivy"]], "Importing libraries": [[48, "Importing-libraries"]], "Let\u2019s build the pipeline with a Tensorflow backend": [[48, "Let's-build-the-pipeline-with-a-Tensorflow-backend"]], "We are using MNIST dataset for this Tutorial": [[48, "We-are-using-MNIST-dataset-for-this-Tutorial"]], "Temporary Dataset and Dynamic loader": [[48, "Temporary-Dataset-and-Dynamic-loader"]], "Defining the Ivy Network": [[48, "Defining-the-Ivy-Network"]], "Training Loop with utility functions": [[48, "Training-Loop-with-utility-functions"]], "Plotting the training metrics": [[48, "Plotting-the-training-metrics"]], "Save the trained Model": [[48, "Save-the-trained-Model"]], "Deepmind PerceiverIO on GPU": [[47, "Deepmind-PerceiverIO-on-GPU"]], "Install Python3.8 and setup the kernel": [[47, "Install-Python3.8-and-setup-the-kernel"]], "Clone the ivy and ivy-models repo": [[47, "Clone-the-ivy-and-ivy-models-repo"]], "Install ivy and ivy_models from the repos": [[47, "Install-ivy-and-ivy_models-from-the-repos"]], "Run the demo\u2026": [[47, "Run-the-demo..."]], "\u2026with torch backend": [[47, "...with-torch-backend"]], "\u2026.with 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