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-rw-r--r--tensorflow/core/ops/ops.pbtxt202
1 files changed, 199 insertions, 3 deletions
diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt
index b90d6b2ddc..56f70f9420 100644
--- a/tensorflow/core/ops/ops.pbtxt
+++ b/tensorflow/core/ops/ops.pbtxt
@@ -2413,6 +2413,56 @@ op {
is_commutative: true
}
op {
+ name: "Erf"
+ input_arg {
+ name: "x"
+ type_attr: "T"
+ }
+ output_arg {
+ name: "y"
+ type_attr: "T"
+ }
+ attr {
+ name: "T"
+ type: "type"
+ allowed_values {
+ list {
+ type: DT_FLOAT
+ type: DT_DOUBLE
+ type: DT_INT32
+ type: DT_COMPLEX64
+ type: DT_INT64
+ }
+ }
+ }
+ summary: "Computes the Gauss error function of `x` element-wise."
+}
+op {
+ name: "Erfc"
+ input_arg {
+ name: "x"
+ type_attr: "T"
+ }
+ output_arg {
+ name: "y"
+ type_attr: "T"
+ }
+ attr {
+ name: "T"
+ type: "type"
+ allowed_values {
+ list {
+ type: DT_FLOAT
+ type: DT_DOUBLE
+ type: DT_INT32
+ type: DT_COMPLEX64
+ type: DT_INT64
+ }
+ }
+ }
+ summary: "Computes the complementary error function of `x` element-wise."
+}
+op {
name: "Exit"
input_arg {
name: "data"
@@ -2949,6 +2999,7 @@ op {
}
summary: "Creates a non-initialized hash table."
description: "This op creates a hash table, specifying the type of its keys and values.\nBefore using the table you will have to initialize it. After initialization the\ntable will be immutable."
+ is_stateful: true
}
op {
name: "HistogramSummary"
@@ -3554,6 +3605,31 @@ op {
summary: "Returns the truth value of (x <= y) element-wise."
}
op {
+ name: "Lgamma"
+ input_arg {
+ name: "x"
+ type_attr: "T"
+ }
+ output_arg {
+ name: "y"
+ type_attr: "T"
+ }
+ attr {
+ name: "T"
+ type: "type"
+ allowed_values {
+ list {
+ type: DT_FLOAT
+ type: DT_DOUBLE
+ type: DT_INT32
+ type: DT_COMPLEX64
+ type: DT_INT64
+ }
+ }
+ }
+ summary: "Computes the log of the absolute value of Gamma of `x` element-wise."
+}
+op {
name: "LinSpace"
input_arg {
name: "start"
@@ -4731,6 +4807,12 @@ op {
number_attr: "Ncontext_dense"
}
input_arg {
+ name: "feature_list_sparse_keys"
+ description: "A list of Nfeature_list_sparse string Tensors\n(scalars). The keys expected in the FeatureLists associated with sparse\nvalues."
+ type: DT_STRING
+ number_attr: "Nfeature_list_sparse"
+ }
+ input_arg {
name: "feature_list_dense_keys"
description: "A list of Nfeature_list_dense string Tensors (scalars).\nThe keys expected in the SequenceExamples\' feature_lists associated\nwith lists of dense values."
type: DT_STRING
@@ -4765,27 +4847,62 @@ op {
type_list_attr: "Tcontext_dense"
}
output_arg {
+ name: "feature_list_sparse_indices"
+ type: DT_INT64
+ number_attr: "Nfeature_list_sparse"
+ }
+ output_arg {
+ name: "feature_list_sparse_values"
+ type_list_attr: "feature_list_sparse_types"
+ }
+ output_arg {
+ name: "feature_list_sparse_shapes"
+ type: DT_INT64
+ number_attr: "Nfeature_list_sparse"
+ }
+ output_arg {
name: "feature_list_dense_values"
type_list_attr: "feature_list_dense_types"
}
attr {
name: "Ncontext_sparse"
type: "int"
+ default_value {
+ i: 0
+ }
has_minimum: true
}
attr {
name: "Ncontext_dense"
type: "int"
+ default_value {
+ i: 0
+ }
+ has_minimum: true
+ }
+ attr {
+ name: "Nfeature_list_sparse"
+ type: "int"
+ default_value {
+ i: 0
+ }
has_minimum: true
}
attr {
name: "Nfeature_list_dense"
type: "int"
+ default_value {
+ i: 0
+ }
has_minimum: true
}
attr {
name: "context_sparse_types"
type: "list(type)"
+ default_value {
+ list {
+ }
+ }
description: "A list of Ncontext_sparse types; the data types of data in\neach context Feature given in context_sparse_keys.\nCurrently the ParseSingleSequenceExample supports DT_FLOAT (FloatList),\nDT_INT64 (Int64List), and DT_STRING (BytesList)."
has_minimum: true
allowed_values {
@@ -4799,6 +4916,10 @@ op {
attr {
name: "Tcontext_dense"
type: "list(type)"
+ default_value {
+ list {
+ }
+ }
has_minimum: true
allowed_values {
list {
@@ -4811,6 +4932,10 @@ op {
attr {
name: "feature_list_dense_types"
type: "list(type)"
+ default_value {
+ list {
+ }
+ }
has_minimum: true
allowed_values {
list {
@@ -4823,12 +4948,37 @@ op {
attr {
name: "context_dense_shapes"
type: "list(shape)"
+ default_value {
+ list {
+ }
+ }
description: "A list of Ncontext_dense shapes; the shapes of data in\neach context Feature given in context_dense_keys.\nThe number of elements in the Feature corresponding to context_dense_key[j]\nmust always equal context_dense_shapes[j].NumEntries().\nThe shape of context_dense_values[j] will match context_dense_shapes[j]."
has_minimum: true
}
attr {
+ name: "feature_list_sparse_types"
+ type: "list(type)"
+ default_value {
+ list {
+ }
+ }
+ description: "A list of Nfeature_list_sparse types; the data types\nof data in each FeatureList given in feature_list_sparse_keys.\nCurrently the ParseSingleSequenceExample supports DT_FLOAT (FloatList),\nDT_INT64 (Int64List), and DT_STRING (BytesList)."
+ has_minimum: true
+ allowed_values {
+ list {
+ type: DT_FLOAT
+ type: DT_INT64
+ type: DT_STRING
+ }
+ }
+ }
+ attr {
name: "feature_list_dense_shapes"
type: "list(shape)"
+ default_value {
+ list {
+ }
+ }
description: "A list of Nfeature_list_dense shapes; the shapes of\ndata in each FeatureList given in feature_list_dense_keys.\nThe shape of each Feature in the FeatureList corresponding to\nfeature_list_dense_key[j] must always equal\nfeature_list_dense_shapes[j].NumEntries()."
has_minimum: true
}
@@ -4986,6 +5136,39 @@ op {
description: "Reduces `input` along the dimensions given in `reduction_indices`. Unless\n`keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in\n`reduction_indices`. If `keep_dims` is true, the reduced dimensions are\nretained with length 1."
}
op {
+ name: "PyFunc"
+ input_arg {
+ name: "input"
+ description: "List of Tensors that will provide input to the Op."
+ type_list_attr: "Tin"
+ }
+ output_arg {
+ name: "output"
+ description: "The outputs from the Op."
+ type_list_attr: "Tout"
+ }
+ attr {
+ name: "token"
+ type: "string"
+ description: "A token representing a registered python function in this address space."
+ }
+ attr {
+ name: "Tin"
+ type: "list(type)"
+ description: "Data types of the inputs to the op."
+ has_minimum: true
+ minimum: 1
+ }
+ attr {
+ name: "Tout"
+ type: "list(type)"
+ description: "Data types of the outputs from the op.\nThe length of the list specifies the number of outputs."
+ has_minimum: true
+ minimum: 1
+ }
+ summary: "Invokes a python function to compute func(input)->output."
+}
+op {
name: "QueueClose"
input_arg {
name: "handle"
@@ -6354,12 +6537,12 @@ op {
name: "ScalarSummary"
input_arg {
name: "tags"
- description: "1-D. Tags for the summary."
+ description: "Tags for the summary."
type: DT_STRING
}
input_arg {
name: "values"
- description: "1-D, same size as `tags. Values for the summary."
+ description: "Same shape as `tags. Values for the summary."
type_attr: "T"
}
output_arg {
@@ -6374,6 +6557,11 @@ op {
list {
type: DT_FLOAT
type: DT_DOUBLE
+ type: DT_INT32
+ type: DT_INT64
+ type: DT_UINT8
+ type: DT_INT16
+ type: DT_INT8
}
}
}
@@ -7806,6 +7994,14 @@ op {
type_attr: "T"
}
attr {
+ name: "validate_indices"
+ type: "bool"
+ default_value {
+ b: true
+ }
+ description: "If true, indices are checked to make sure they are sorted in\nlexicographic order and that there are no repeats."
+ }
+ attr {
name: "T"
type: "type"
}
@@ -7820,7 +8016,7 @@ op {
}
}
summary: "Converts a sparse representation into a dense tensor."
- description: "Builds an array `dense` with shape `output_shape` such that\n\n```prettyprint\n# If sparse_indices is scalar\ndense[i] = (i == sparse_indices ? sparse_values : default_value)\n\n# If sparse_indices is a vector, then for each i\ndense[sparse_indices[i]] = sparse_values[i]\n\n# If sparse_indices is an n by d matrix, then for each i in [0, n)\ndense[sparse_indices[i][0], ..., sparse_indices[i][d-1]] = sparse_values[i]\n```\n\nAll other values in `dense` are set to `default_value`. If `sparse_values` is a\nscalar, all sparse indices are set to this single value."
+ description: "Builds an array `dense` with shape `output_shape` such that\n\n```prettyprint\n# If sparse_indices is scalar\ndense[i] = (i == sparse_indices ? sparse_values : default_value)\n\n# If sparse_indices is a vector, then for each i\ndense[sparse_indices[i]] = sparse_values[i]\n\n# If sparse_indices is an n by d matrix, then for each i in [0, n)\ndense[sparse_indices[i][0], ..., sparse_indices[i][d-1]] = sparse_values[i]\n```\n\nAll other values in `dense` are set to `default_value`. If `sparse_values` is a\nscalar, all sparse indices are set to this single value.\n\nIndices should be sorted in lexicographic order, and indices must not\ncontain any repeats. If `validate_indices` is true, these properties\nare checked during execution."
}
op {
name: "Split"
generated by cgit v1.2.3 (git 2.39.1) at 2024-06-27 23:42:49 +0000