# tf.raw_ops.CropAndResizeGradImage

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Computes the gradient of the crop_and_resize op wrt the input image tensor.

#### View aliases


**Compat aliases for migration**

See
[Migration guide](https://www.tensorflow.org/guide/migrate) for
more details.

[`tf.compat.v1.raw_ops.CropAndResizeGradImage`](https://www.tensorflow.org/api_docs/python/tf/raw_ops/CropAndResizeGradImage)

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    tf.raw_ops.CropAndResizeGradImage(
        grads,
        boxes,
        box_ind,
        image_size,
        T,
        method='bilinear',
        name=None
    )

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| ## Args ||
|---|---|---|
| `grads` | A `Tensor` of type `float32`. A 4-D tensor of shape `[num_boxes, crop_height, crop_width, depth]`. |
| `boxes` | A `Tensor` of type `float32`. A 2-D tensor of shape `[num_boxes, 4]`. The `i`-th row of the tensor specifies the coordinates of a box in the `box_ind[i]` image and is specified in normalized coordinates `[y1, x1, y2, x2]`. A normalized coordinate value of `y` is mapped to the image coordinate at `y * (image_height - 1)`, so as the `[0, 1]` interval of normalized image height is mapped to `[0, image_height - 1] in image height coordinates. We do allow y1 > y2, in which case the sampled crop is an up-down flipped version of the original image. The width dimension is treated similarly. Normalized coordinates outside the`\[0, 1\]`range are allowed, in which case we use`extrapolation_value`to extrapolate the input image values. </td> </tr><tr> <td>`box_ind`<a id="box_ind"></a> </td> <td> A`Tensor`of type`int32`. A 1-D tensor of shape`\[num_boxes\]`with int32 values in`\[0, batch)`. The value of`box_ind\[i\]`specifies the image that the`i`-th box refers to. </td> </tr><tr> <td>`image_size`<a id="image_size"></a> </td> <td> A`Tensor`of type`int32`. A 1-D tensor with value`\[batch, image_height, image_width, depth\]`containing the original image size. Both`image_height`and`image_width`need to be positive. </td> </tr><tr> <td>`T`<a id="T"></a> </td> <td> A <a href="../../tf/dtypes/DType"><code>tf.DType</code></a> from:`tf.float32, tf.half, tf.float64`. </td> </tr><tr> <td>`method`<a id="method"></a> </td> <td> An optional`string`from:`"bilinear", "nearest"`. Defaults to`"bilinear"`. A string specifying the interpolation method. Only 'bilinear' is supported for now. </td> </tr><tr> <td>`name\` | A name for the operation (optional). |

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| ## Returns ||
|---|---|
| A `Tensor` of type `T`. ||

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