neulab/docprompting-conala huggingface dataset:
tf.raw_ops.ScalarSummary Outputs a Summary protocol buffer with scalar values.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ScalarSummary  
tf.raw_ops.ScalarSummary(
    tags, values, name=None
)
 The input tags and values must have the same shape. The generated summary has a summary value for each tag-value pair in tags and values.
 


 Args
  tags   A Tensor of type string. Tags for the summary.  
  values   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64. Same shape as tags. Values for the summary. </td> </tr><tr> <td>name`   A name for the operation (optional).   
 


 Returns   A Tensor of type string.
tf.raw_ops.ScaleAndTranslate  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ScaleAndTranslate  
tf.raw_ops.ScaleAndTranslate(
    images, size, scale, translation, kernel_type='lanczos3',
    antialias=True, name=None
)

 


 Args
  images   A Tensor. Must be one of the following types: int8, uint8, int16, uint16, int32, int64, bfloat16, half, float32, float64.  
  size   A Tensor of type int32.  
  scale   A Tensor of type float32.  
  translation   A Tensor of type float32.  
  kernel_type   An optional string. Defaults to "lanczos3".  
  antialias   An optional bool. Defaults to True.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type float32.
tf.raw_ops.ScaleAndTranslateGrad  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ScaleAndTranslateGrad  
tf.raw_ops.ScaleAndTranslateGrad(
    grads, original_image, scale, translation, kernel_type='lanczos3',
    antialias=True, name=None
)

 


 Args
  grads   A Tensor. Must be one of the following types: float32.  
  original_image   A Tensor. Must have the same type as grads.  
  scale   A Tensor of type float32.  
  translation   A Tensor of type float32.  
  kernel_type   An optional string. Defaults to "lanczos3".  
  antialias   An optional bool. Defaults to True.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as grads.
tf.raw_ops.ScanDataset Creates a dataset successively reduces f over the elements of input_dataset.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ScanDataset  
tf.raw_ops.ScanDataset(
    input_dataset, initial_state, other_arguments, f, output_types, output_shapes,
    preserve_cardinality=False, use_default_device=True, name=None
)

 


 Args
  input_dataset   A Tensor of type variant.  
  initial_state   A list of Tensor objects.  
  other_arguments   A list of Tensor objects.  
  f   A function decorated with @Defun.  
  output_types   A list of tf.DTypes that has length >= 1.  
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.  
  preserve_cardinality   An optional bool. Defaults to False.  
  use_default_device   An optional bool. Defaults to True.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor of type variant.
tf.raw_ops.ScatterAdd Adds sparse updates to a variable reference.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ScatterAdd  
tf.raw_ops.ScatterAdd(
    ref, indices, updates, use_locking=False, name=None
)
 This operation computes # Scalar indices
ref[indices, ...] += updates[...]

# Vector indices (for each i)
ref[indices[i], ...] += updates[i, ...]

# High rank indices (for each i, ..., j)
ref[indices[i, ..., j], ...] += updates[i, ..., j, ...]
 This operation outputs ref after the update is done. This makes it easier to chain operations that need to use the reset value. Duplicate entries are handled correctly: if multiple indices reference the same location, their contributions add. Requires updates.shape = indices.shape + ref.shape[1:] or updates.shape = [].   
 


 Args
  ref   A mutable Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. Should be from a Variable node.  
  indices   A Tensor. Must be one of the following types: int32, int64. A tensor of indices into the first dimension of ref.  
  updates   A Tensor. Must have the same type as ref. A tensor of updated values to add to ref.  
  use_locking   An optional bool. Defaults to False. If True, the addition will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.  
  name   A name for the operation (optional).   
 


 Returns   A mutable Tensor. Has the same type as ref.
tf.raw_ops.ScatterDiv Divides a variable reference by sparse updates.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ScatterDiv  
tf.raw_ops.ScatterDiv(
    ref, indices, updates, use_locking=False, name=None
)
 This operation computes # Scalar indices
ref[indices, ...] /= updates[...]

# Vector indices (for each i)
ref[indices[i], ...] /= updates[i, ...]

# High rank indices (for each i, ..., j)
ref[indices[i, ..., j], ...] /= updates[i, ..., j, ...]
 This operation outputs ref after the update is done. This makes it easier to chain operations that need to use the reset value. Duplicate entries are handled correctly: if multiple indices reference the same location, their contributions divide. Requires updates.shape = indices.shape + ref.shape[1:] or updates.shape = [].
 


 Args
  ref   A mutable Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. Should be from a Variable node.  
  indices   A Tensor. Must be one of the following types: int32, int64. A tensor of indices into the first dimension of ref.  
  updates   A Tensor. Must have the same type as ref. A tensor of values that ref is divided by.  
  use_locking   An optional bool. Defaults to False. If True, the operation will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.  
  name   A name for the operation (optional).   
 


 Returns   A mutable Tensor. Has the same type as ref.
tf.raw_ops.ScatterMax Reduces sparse updates into a variable reference using the max operation.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ScatterMax  
tf.raw_ops.ScatterMax(
    ref, indices, updates, use_locking=False, name=None
)
 This operation computes # Scalar indices
ref[indices, ...] = max(ref[indices, ...], updates[...])

# Vector indices (for each i)
ref[indices[i], ...] = max(ref[indices[i], ...], updates[i, ...])

# High rank indices (for each i, ..., j)
ref[indices[i, ..., j], ...] = max(ref[indices[i, ..., j], ...], updates[i, ..., j, ...])
 This operation outputs ref after the update is done. This makes it easier to chain operations that need to use the reset value. Duplicate entries are handled correctly: if multiple indices reference the same location, their contributions combine. Requires updates.shape = indices.shape + ref.shape[1:] or updates.shape = [].   
 


 Args
  ref   A mutable Tensor. Must be one of the following types: half, bfloat16, float32, float64, int32, int64. Should be from a Variable node.  
  indices   A Tensor. Must be one of the following types: int32, int64. A tensor of indices into the first dimension of ref.  
  updates   A Tensor. Must have the same type as ref. A tensor of updated values to reduce into ref.  
  use_locking   An optional bool. Defaults to False. If True, the update will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.  
  name   A name for the operation (optional).   
 


 Returns   A mutable Tensor. Has the same type as ref.
tf.raw_ops.ScatterMin Reduces sparse updates into a variable reference using the min operation.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ScatterMin  
tf.raw_ops.ScatterMin(
    ref, indices, updates, use_locking=False, name=None
)
 This operation computes # Scalar indices
ref[indices, ...] = min(ref[indices, ...], updates[...])

# Vector indices (for each i)
ref[indices[i], ...] = min(ref[indices[i], ...], updates[i, ...])

# High rank indices (for each i, ..., j)
ref[indices[i, ..., j], ...] = min(ref[indices[i, ..., j], ...], updates[i, ..., j, ...])
 This operation outputs ref after the update is done. This makes it easier to chain operations that need to use the reset value. Duplicate entries are handled correctly: if multiple indices reference the same location, their contributions combine. Requires updates.shape = indices.shape + ref.shape[1:] or updates.shape = [].   
 


 Args
  ref   A mutable Tensor. Must be one of the following types: half, bfloat16, float32, float64, int32, int64. Should be from a Variable node.  
  indices   A Tensor. Must be one of the following types: int32, int64. A tensor of indices into the first dimension of ref.  
  updates   A Tensor. Must have the same type as ref. A tensor of updated values to reduce into ref.  
  use_locking   An optional bool. Defaults to False. If True, the update will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.  
  name   A name for the operation (optional).   
 


 Returns   A mutable Tensor. Has the same type as ref.
tf.raw_ops.ScatterMul Multiplies sparse updates into a variable reference.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ScatterMul  
tf.raw_ops.ScatterMul(
    ref, indices, updates, use_locking=False, name=None
)
 This operation computes # Scalar indices
ref[indices, ...] *= updates[...]

# Vector indices (for each i)
ref[indices[i], ...] *= updates[i, ...]

# High rank indices (for each i, ..., j)
ref[indices[i, ..., j], ...] *= updates[i, ..., j, ...]
 This operation outputs ref after the update is done. This makes it easier to chain operations that need to use the reset value. Duplicate entries are handled correctly: if multiple indices reference the same location, their contributions multiply. Requires updates.shape = indices.shape + ref.shape[1:] or updates.shape = [].
 


 Args
  ref   A mutable Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. Should be from a Variable node.  
  indices   A Tensor. Must be one of the following types: int32, int64. A tensor of indices into the first dimension of ref.  
  updates   A Tensor. Must have the same type as ref. A tensor of updated values to multiply to ref.  
  use_locking   An optional bool. Defaults to False. If True, the operation will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.  
  name   A name for the operation (optional).   
 


 Returns   A mutable Tensor. Has the same type as ref.
tf.raw_ops.ScatterNdAdd Applies sparse addition to individual values or slices in a Variable.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ScatterNdAdd  
tf.raw_ops.ScatterNdAdd(
    ref, indices, updates, use_locking=False, name=None
)
 ref is a Tensor with rank P and indices is a Tensor of rank Q. indices must be integer tensor, containing indices into ref. It must be shape [d_0, ..., d_{Q-2}, K] where 0 < K <= P. The innermost dimension of indices (with length K) corresponds to indices into elements (if K = P) or slices (if K < P) along the Kth dimension of ref. updates is Tensor of rank Q-1+P-K with shape: [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]]
 For example, say we want to add 4 scattered elements to a rank-1 tensor to 8 elements. In Python, that addition would look like this: ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8])
indices = tf.constant([[4], [3], [1], [7]])
updates = tf.constant([9, 10, 11, 12])
add = tf.scatter_nd_add(ref, indices, updates)
with tf.Session() as sess:
  print sess.run(add)
 The resulting update to ref would look like this: [1, 13, 3, 14, 14, 6, 7, 20]
 See tf.scatter_nd for more details about how to make updates to slices.
 


 Args
  ref   A mutable Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. A mutable Tensor. Should be from a Variable node.  
  indices   A Tensor. Must be one of the following types: int32, int64. A Tensor. Must be one of the following types: int32, int64. A tensor of indices into ref.  
  updates   A Tensor. Must have the same type as ref. A Tensor. Must have the same type as ref. A tensor of updated values to add to ref.  
  use_locking   An optional bool. Defaults to False. An optional bool. Defaults to True. If True, the assignment will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.  
  name   A name for the operation (optional).   
 


 Returns   A mutable Tensor. Has the same type as ref.
tf.raw_ops.ScatterNdMax Computes element-wise maximum.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ScatterNdMax  
tf.raw_ops.ScatterNdMax(
    ref, indices, updates, use_locking=False, name=None
)

 


 Args
  ref   A mutable Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. A mutable Tensor. Should be from a Variable node.  
  indices   A Tensor. Must be one of the following types: int32, int64. A Tensor. Must be one of the following types: int32, int64. A tensor of indices into ref.  
  updates   A Tensor. Must have the same type as ref. A Tensor. Must have the same type as ref. A tensor of updated values to add to ref.  
  use_locking   An optional bool. Defaults to False. An optional bool. Defaults to True. If True, the assignment will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.  
  name   A name for the operation (optional).   
 


 Returns   A mutable Tensor. Has the same type as ref.
tf.raw_ops.ScatterNdMin Computes element-wise minimum.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ScatterNdMin  
tf.raw_ops.ScatterNdMin(
    ref, indices, updates, use_locking=False, name=None
)

 


 Args
  ref   A mutable Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. A mutable Tensor. Should be from a Variable node.  
  indices   A Tensor. Must be one of the following types: int32, int64. A Tensor. Must be one of the following types: int32, int64. A tensor of indices into ref.  
  updates   A Tensor. Must have the same type as ref. A Tensor. Must have the same type as ref. A tensor of updated values to add to ref.  
  use_locking   An optional bool. Defaults to False. An optional bool. Defaults to True. If True, the assignment will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.  
  name   A name for the operation (optional).   
 


 Returns   A mutable Tensor. Has the same type as ref.
tf.raw_ops.ScatterNdNonAliasingAdd   Applies sparse addition to input using individual values or slices  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ScatterNdNonAliasingAdd  
tf.raw_ops.ScatterNdNonAliasingAdd(
    input, indices, updates, name=None
)
 from updates according to indices indices. The updates are non-aliasing: input is only modified in-place if no other operations will use it. Otherwise, a copy of input is made. This operation has a gradient with respect to both input and updates. input is a Tensor with rank P and indices is a Tensor of rank Q. indices must be integer tensor, containing indices into input. It must be shape \([d_0, ..., d_{Q-2}, K]\) where 0 < K <= P. The innermost dimension of indices (with length K) corresponds to indices into elements (if K = P) or (P-K)-dimensional slices (if K < P) along the Kth dimension of input. updates is Tensor of rank Q-1+P-K with shape:  $$[d_0, ..., d_{Q-2}, input.shape[K], ..., input.shape[P-1]].$$  For example, say we want to add 4 scattered elements to a rank-1 tensor to 8 elements. In Python, that addition would look like this: input = tf.constant([1, 2, 3, 4, 5, 6, 7, 8])
indices = tf.constant([[4], [3], [1], [7]])
updates = tf.constant([9, 10, 11, 12])
output = tf.scatter_nd_non_aliasing_add(input, indices, updates)
with tf.Session() as sess:
  print(sess.run(output))
 The resulting value output would look like this: [1, 13, 3, 14, 14, 6, 7, 20]
 See tf.scatter_nd for more details about how to make updates to slices.
 


 Args
  input   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64, bool. A Tensor.  
  indices   A Tensor. Must be one of the following types: int32, int64. A Tensor. Must be one of the following types: int32, int64. A tensor of indices into input.  
  updates   A Tensor. Must have the same type as input. A Tensor. Must have the same type as ref. A tensor of updated values to add to input.  
  name   A name for the operation (optional).   
 


 Returns   A Tensor. Has the same type as input.
tf.raw_ops.ScatterNdSub Applies sparse subtraction to individual values or slices in a Variable.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ScatterNdSub  
tf.raw_ops.ScatterNdSub(
    ref, indices, updates, use_locking=False, name=None
)
 within a given variable according to indices. ref is a Tensor with rank P and indices is a Tensor of rank Q. indices must be integer tensor, containing indices into ref. It must be shape [d_0, ..., d_{Q-2}, K] where 0 < K <= P. The innermost dimension of indices (with length K) corresponds to indices into elements (if K = P) or slices (if K < P) along the Kth dimension of ref. updates is Tensor of rank Q-1+P-K with shape: [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]]
 For example, say we want to subtract 4 scattered elements from a rank-1 tensor with 8 elements. In Python, that subtraction would look like this: ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8])
indices = tf.constant([[4], [3], [1], [7]])
updates = tf.constant([9, 10, 11, 12])
sub = tf.scatter_nd_sub(ref, indices, updates)
with tf.Session() as sess:
  print sess.run(sub)
 The resulting update to ref would look like this: [1, -9, 3, -6, -4, 6, 7, -4]
 See tf.scatter_nd for more details about how to make updates to slices.
 


 Args
  ref   A mutable Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. A mutable Tensor. Should be from a Variable node.  
  indices   A Tensor. Must be one of the following types: int32, int64. A Tensor. Must be one of the following types: int32, int64. A tensor of indices into ref.  
  updates   A Tensor. Must have the same type as ref. A Tensor. Must have the same type as ref. A tensor of updated values to subtract from ref.  
  use_locking   An optional bool. Defaults to False. An optional bool. Defaults to True. If True, the assignment will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.  
  name   A name for the operation (optional).   
 


 Returns   A mutable Tensor. Has the same type as ref.
tf.raw_ops.ScatterNdUpdate   Applies sparse updates to individual values or slices within a given  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ScatterNdUpdate  
tf.raw_ops.ScatterNdUpdate(
    ref, indices, updates, use_locking=True, name=None
)
 variable according to indices. ref is a Tensor with rank P and indices is a Tensor of rank Q. indices must be integer tensor, containing indices into ref. It must be shape \([d_0, ..., d_{Q-2}, K]\) where 0 < K <= P. The innermost dimension of indices (with length K) corresponds to indices into elements (if K = P) or slices (if K < P) along the Kth dimension of ref. updates is Tensor of rank Q-1+P-K with shape:  $$[d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]].$$  For example, say we want to update 4 scattered elements to a rank-1 tensor to 8 elements. In Python, that update would look like this: ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8])
indices = tf.constant([[4], [3], [1] ,[7]])
updates = tf.constant([9, 10, 11, 12])
update = tf.scatter_nd_update(ref, indices, updates)
with tf.Session() as sess:
  print sess.run(update)
 The resulting update to ref would look like this: [1, 11, 3, 10, 9, 6, 7, 12]
 See tf.scatter_nd for more details about how to make updates to slices. See also tf.scatter_update and tf.batch_scatter_update.
 


 Args
  ref   A mutable Tensor. A mutable Tensor. Should be from a Variable node.  
  indices   A Tensor. Must be one of the following types: int32, int64. A Tensor. Must be one of the following types: int32, int64. A tensor of indices into ref.  
  updates   A Tensor. Must have the same type as ref. A Tensor. Must have the same type as ref. A tensor of updated values to add to ref.  
  use_locking   An optional bool. Defaults to True. An optional bool. Defaults to True. If True, the assignment will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.  
  name   A name for the operation (optional).   
 


 Returns   A mutable Tensor. Has the same type as ref.
tf.raw_ops.ScatterSub Subtracts sparse updates to a variable reference.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.raw_ops.ScatterSub  
tf.raw_ops.ScatterSub(
    ref, indices, updates, use_locking=False, name=None
)
 # Scalar indices
ref[indices, ...] -= updates[...]

# Vector indices (for each i)
ref[indices[i], ...] -= updates[i, ...]

# High rank indices (for each i, ..., j)
ref[indices[i, ..., j], ...] -= updates[i, ..., j, ...]
 This operation outputs ref after the update is done. This makes it easier to chain operations that need to use the reset value. Duplicate entries are handled correctly: if multiple indices reference the same location, their (negated) contributions add. Requires updates.shape = indices.shape + ref.shape[1:] or updates.shape = [].   
 


 Args
  ref   A mutable Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. Should be from a Variable node.  
  indices   A Tensor. Must be one of the following types: int32, int64. A tensor of indices into the first dimension of ref.  
  updates   A Tensor. Must have the same type as ref. A tensor of updated values to subtract from ref.  
  use_locking   An optional bool. Defaults to False. If True, the subtraction will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.  
  name   A name for the operation (optional).   
 


 Returns   A mutable Tensor. Has the same type as ref.